Revitalization efforts are ongoing and include, but are not limited to: digitizing transcribed language and stories, a phonological analysis of Miluk, the creation of pedagogical materials for language teaching, and partnership with the University of Oregon to make BA-satisfying language courses in both languages. Another core issue presented by the community is that of linguistic relatedness. There is a sense within the community that Miluk and Hanis Coos are sister languages. Indeed, those who have worked on the languages, from Leo Frachtenburg to Melville Jacobs to John Peabody Harrington, have all noted that the languages are quite similar and that they are likely related. It is this question of genetic relatedness that this work seeks to answer. This is done by creating a cognate list and creating regular sound correspondences as evidence of linguistic relatedness. Having done so, it is the belief of this author that Hanis and Miluk are related and distinct languages and not dialects of the same language. The remainder of this paper is structured as follows. Section 2 provides a brief overview of the Proto-Coosan Hypothesis and other historical views on Hanis and Miluk relatedness. Section 3 reviews the sources used in this work. Section 4 goes through the sound correspondences that we have for Hanis and Miluk. Section 5 goes over confounding reconstructions. Finally, section 6 provides a discussion of the findings and possible future work. The Proto-Coosan hypothesis, quite simply, is that Hanis and Miluk make up the Coosan language family.
It states that they are related, yet distinct languages. This is in opposition to an alternative hypothesis,nursery grow bag the single Coosan language hypothesis, that has Hanis and Miluk as two dialects of the same language. Another explanation that has been posited is that they are completely unrelated to each other, and that any resemblance comes from prolonged contact. Yet another explanation puts them in the same family but within a larger Oregon Coast Penutian. Finally, the last alternative hypothesis posits that they both may or may not be related but that Miluk is part of a top-level branch of a family that includes Proto-Salishan. Frachtenburg gives the first published discussion of Hanis and Miluk.In this, he presents the dialect hypothesis, stating: ‘Of the two principal dialects, Hanis and Miluk, the latter is now practically extinct; while the former is still spoken by about thirty individuals, whose number is steadily decreasing’. He also noted significant differences between the languages, saying of Miluk: ‘this dialect exhibits only in a most general way the characteristic traits of the Kusan stock. Otherwise it is vastly different from Hanis in etymological and even lexicographical respects.’ It should be noted that the exact meaning of the term ‘dialect’ as it is understood today had not been established at that time. Jacobs would also go on to refer to Hanis and Miluk as dialects of the Coosan language . However he makes two extremely important points regarding the two languages, both of which were made possible due to the outstanding aid of his consultant Mrs. Annie Miner Peterson. The first of these observations is that the languages were nearly mutually unintelligible .
The second observation is that both Hanis and Miluk appeared to have distinct varieties themselves, with the Miluk specifically having a distinction between ‘Lower Coquille’ and ‘South Slough’ varieties . While these observations themselves do not conclusively establish Hanis and Miluk as distinct languages they do serve as evidence towards that conclusion. There are some who agree with Sapir, namely, Delancey and Golla . That said, these scholars believe that a significant amount of comparative work still needs to be done to fully realize this complex ‘Oregon Coast Penutian’ branch. Other scholars have disagreed with this, placing Coosan as a single family separate from other families grouped within the Penutian hypothesis . Yet there are some who doubt the relatedness of Hanis and Miluk altogether. Namely, Pierce speculates that the languages are in fact unrelated. To this end, he discusses the lack of geographic barriers that would be expected in the creation of dialects and the fact that the best evidence we have of Miluk comes from Mrs. Annie Miner Peterson, who also spoke Hanis. He also notes that the languages do not share ‘74% of their vocabulary’, which was based on Dorsey’s word lists . He believes that these facts, together, point to a situation of extensive language contact that allowed for ‘convergent’ evolution of the two languages . More recent work by Doty takes a different approach. Doty focuses his efforts specifically on the classification of Miluk. In his dissertation, he lays out an argument for similarities between Miluk and the Salishan languages, which are located principally in modern day Washington, and whose southernmost member is Tillamook, of which the area of the same name is some 166 miles north of Coos Bay.
Doty’s argument is unpersuasive in showing genetic relatedness of Miluk with Salishan languages, which he acknowledges, saying in his presentation of words that appear to be similar between Miluk and Salishan languages that ‘[only]… appear to exhibit regular sound correspondences’ . He does do a decent job, however, showing the influence and contact that must have existed between Miluk and Salishan languages. Doty does not incorporate Hanis significantly into this discussion, as it does not share the same similarities with Proto-Salishan. Ultimately though, he does not take a stance one way or the other as to whether Hanis and Miluk are related, other than to say that he believes that they are not Penutian . In my work with the Hanis and Miluk peoples of both the CLUSI and Coquille Tribes, there does appear to be a consensus that Hanis and Miluk are distinct, yet related, languages. It is because of this belief that I was asked to reconstruct Proto-Coosan and provide the evidence through regular sound correspondences that Hanis and Miluk are both related and distinctive enough to be considered different languages. The data used for this project comes from the field notes of Harry Hull St. Clair . St. Clair was an assistant to Frachtenberg, who wrote a grammatical sketch of Hanis. St. Clair is known and respected for having a good ear within the community and by the scholars who have worked with the languages. During his time in Coos Bay he was working primarily to document stories and words in Hanis for Frachtenberg. However, while doing so, he ended up collecting a comparative word list of 222 words in both languages. Jim Buchanan, who would also serve as a speaker for Harrington, acted as St. Clair’s speaker for Hanis. George Barney served as St. Clair’s speaker for Miluk. St. Clair’s transcriptions were all done phonetically, and as such,plastic growing bag they have more detail than would normally be used for a historical reconstruction. Both the Miluk and Hanis Coos transcriptions are adjusted here for the accepted phonological analyses of each language. For Hanis, Frachtenberg’s analysis in his grammatical sketch is used, and for Miluk, Douglas-Tavani’s phonological analysis. From the word lists transcribed by St. Clair, cognate sets were created by a University of Oregon undergraduate research assistant, Hana Wikum, under Enna Helms. Transcribed words were converted into IPA from St. Clair’s orthography by Douglas-Tavani’s research assistant, University of California, Santa Barbara undergraduate student Zoe Fang. Also included as a source within this analysis are the ‘Jacobs’ slipfiles’. These are the word lists that Jacobs collected from Mrs. Annie Miner Peterson of both Hanis and Miluk. Jacobs’ slipfiles are far more extensive than any other collection of Hanis and Miluk, with thousands of entries. Due to the vast size of these lists not every cognate set was included. Instead, more basic terms were collected. The inclusion of these cognate sets was also done to capture certain sounds, namely uvular consonants, which while noted by everyone who has worked with the languages, were almost entirely absent from St. Clair’s word list. This does not appear to be due to an inability on the part of St. Clair to hear these sounds, but rather, him simply not collecting words with these consonants.As both St. Clair and Jacobs transcribed phonetically rather than phonemically, reconstruction was somewhat more difficult. The data from both languages was compared to their respective phonological analyses and adjusted accordingly. This was minimal for the Hanis examples. The Miluk data underwent significantly more adjustments, namely, all instances of [ɑ] are treated as /a/ and all instances of [æ] are treated as /ɛ/, reflecting their free variation in the Miluk of Mrs. Annie Miner Peterson .
The only departures from Douglas-Tavani’s phonological analysis of Miluk is the inclusion of geminate resonants which were not observed in that analysis butwhich do appear in the Jacobs’ slipfiles as distinctive and the addition of /dl/. This addition was also made for Hanis, which is a departure from Frachtenberg but in line with Jacobs’ observations and the areal commonality of this patterning with the other lateral affricates . The phonological inventories of both languages are given in Figure 1. Many occur only one time. There are a number of possible causes of this: mistranscription, normal free variation yet undescribed, allophonic variations yet undescribed, and differences in transcriptions between transcribers and by the same transcriber over time. That said, this work assumes, given lack of evidence to the contrary and the present inability to test these examples with natively fluent speakers, that the transcriptions of St. Clair and Jacobs are true and accurate transcriptions of the speakers they were working with. Additionally, where regular sound changes are presented with only one example, these are indeed believed to be regular. The single examples reflect the limited amount of data available. If more data is later added to these data, it is believed that these regular changes would hold. With that in mind, it could also be that some of these environments would not hold, relegating such changes as either irregular or confounding correspondences. As the global population continues to expand, “it is estimated that food production will need to increase by 60% by 2050 to feed the estimated 10 billion people expected on Earth. An increase in production along with a reduction in food loss due to pests and pathogens and food waste will be needed to meet demand”. Crop loss resulting from plant diseases and pests poses a formidable challenge for crop growers worldwide. Plant diseases and pests lower the product quality or shelf-life of crops, decrease the nutritional value of vegetables and fruits, and reduce crop yield. Plant Diseases caused by fungal pathogens can cause crop losses of 10% to 20% each year. The Food and Agriculture Organization of the United Nations estimates that annually 20% to 40% of global crop production are lost to pests. Each year, plant diseases cost the global economy around $220 billion USD, and invasive insects around $70 billion USD. A challenge crop growers face is accurately identifying the disease responsible for their crop losses. The identification process is particularly challenging as some plant diseases exhibit similar symptoms, particularly during the early stages of infection. Consequently, discerning the nuanced distinctions becomes a daunting task for the human eye. Often, crop growers can recognize the disease after it has significantly affected their crops or when the infection or infestation has persisted over a prolonged period of time, leading to observable alterations in leaf appearance or crop loss. It is crucial to emphasize the significance of proper disease identification, as employing the wrong treatment can be a waste of time, financial resources, and possibly cause further crop loss or damage.In order to facilitate the identification of plant diseases, Mohanty et al. proposed a novel approach in their scholarly work titled “Using Deep Learning for Image-Based Plant Disease Classification”. The researchers explored the utilization of deep learning convolutional neural network models to effectively discern various types of plant diseases. The data set in their study was obtained from the PlantVillage project, encompassing a vast collection of 54,306 color images depicting 14 distinct crop species afflicted with 20 different disease types or healthy conditions. The authors conducted an extensive investigation, comparing the effectiveness of using color images versus gray-scale and segmented images, exploring various training validation-testing splits, comparing the outcomes of training models from scratch versus utilizing pre-trained models, and evaluating the performance of GoogLeNet and AlexNet, two different deep learning convolutional neural network architectures.