Fish–People–Place:Interweaving Knowledges to Elucidate Pacific Salmon Fate
Bibliographic record
Abstract
Migratory organisms carry high ecological and cultural significance as their cyclic movements through time and space create influxes of nutrients into ecosystems and provide important sources of food to people -imprinting on cultures, bodies of practice and management as well as knowledge systems.However, their often long-distance movements between habitats expose them to multiple and potentially interacting risks.Migratory Pacific salmon (Oncorhynchus spp.) are threatened by a suite of stressors, both known (e.g., overfishing, climate change) and unknown, that jeopardize their wellbeing as well as that of linked social-ecological systems.A central focus of this thesis is to elucidate the ultimate fate (i.e., survival to spawning grounds) of salmon who 1 encounter fishing gears but either escape or are released as bycatch, and how this fate is shaped by other factors at play (such as rising temperatures).To gain an improved understanding of what other potential factors may be, a second focus here is to identify leading threats endangering salmon and aquatic ecosystems more generally.Different ways of knowing are valued and interwoven in this work, motivated by the Mi'kmaw conceptual framework of Etuaptmumk or "Two-Eyed Seeing" which creates a pathway for learning from both Indigenous and Western sciences, using their distinct strengths and methodologies in tandem.Experimental fisheries approaches, carried out in partnership with local and Indigenous fishers and fisheries managers, reveal that the context of salmon capture significantly Nat'oot'ten, Peters, Stó:lō, Secwépemc, St'át'imc, Tŝilhqot'in, Ts'msyen and xʷməθkʷəy̓ əm Nations who welcomed me to their territories and shared with me their love of wild salmon and healthy rivers.I am forever grateful to the Nisga'a Nation, communities, citizens and knowledge keepers who have so warmly welcomed me home to the Nass River Valley.My gratitude also goes to the fish and Lisims (Nass River) who are at the very heart of it all.The Nisga'a Nation has played a central role in supporting me throughout my studies, and I express my thanks both to them and the Gingolx Village Government's Education Department, especially Renee Garner who encouraged me at every turn and spawned the idea for our annual salmon science camps that have been a highlight of my PhD.I also thank the other major funding sources who have made significant investments in me as an early career scientist, including Carleton University, Indspire, the New
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".