Aboriginal fisher perspectives on use of biotelemetry technology to study adult Pacific salmon
Bibliographic record
Abstract
Biotelemetry has become a popular tool accepted by the scientific community as a reliable approach for studying wild fish. However, stakeholder perspectives on scientific techniques and the information they generate are not uniformly positive. Aboriginal groups in particular may have opposition or apprehension to telemetry as a research tool. To that end, we conducted a river-bank survey of 111 aboriginal First Nations fishers that target adult Pacific salmon in the lower Fraser River, British Columbia, Canada. The majority of respondents had heard of telemetry, but few had knowledge of its function. Most responses regarding the use of telemetry in fisheries science were positive. The few negative perspectives were primarily concerned about the effects of tagging procedures whereas positive perspectives arose because telemetry was perceived to generate information on migration patterns and survival. Over half of the respondents would trust data arising from telemetry studies, but some had conditions related to the group conducting the research and their experience with fish handling. Several respondents noted the need for additional consultation and outreach with aboriginal communities (especially fishers) to better inform them of study questions and techniques which, in the case of telemetry studies, could promote better participation in tag return programs and uptake of knowledge emanating from use of telemetry.
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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".