Peering into the Past: Species Identification of Archaeological Pacific Salmon on Southwest Vancouver Island
Bibliographic record
Abstract
With anthropogenic climate change accelerating, environmental scientists, historical ecologists, and fisheries scientists alike have been asking questions about the future of our oceans. Understanding Pacific salmonid species composition at archaeological sites through very long-time horizons could provide answers to some of those questions. Archaeological studies of five species of northeast Pacific salmon (Oncorhynchus gorbuscha, O. nerka, O. keta, O. tshawytscha, and O. kisutch) on the Northwest Coast have become increasingly important for understanding the historical distribution and exploitation of these significant cultural and ecological species. This is a regionally grounded study utilising archaeological salmon vertebrae collected from the Tseshaht village site of Kakmakimih on the southwest coast of British Columbia. Vertebral morphometric analysis has been proposed as an inexpensive, non-destructive supplementary method to other more established methods of identification (ancient DNA testing, and collagen peptide analysis [ZooMS] to infer or identify salmon species in archaeological assemblages. I apply the method of vertebral morphometric measurements to characterise and identify archaeological salmon species from vertebrae. I also investigate morphometric variability throughout the vertebral column and apply the morphometric measurements method to anatomically ordered vertebrae from modern salmonid specimens. Through data exploration and statistical analysis, I find that vertebral morphometric analysis has the potential to refine salmon species identifications in archaeological assemblages. This methodological approach contributes to the broader theme of evolution and ecology in anthropology by providing insight into human-non-human relationships in the past and present, and by reconstructing salmon populations that are so crucial to Indigenous fisheries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".