Data from: Insights on the concept of indicator populations derived from parentage-based tagging in a large-scale coho salmon application in British Columbia, Canada
Bibliographic record
Abstract
For Pacific salmon, the key fisheries management goal in British Columbia (BC) is to maintain and restore healthy and diverse Pacific salmon populations, making conservation of salmon biodiversity the highest priority for resource management decision-making. Salmon status assessments are often conducted on coded-wire tagged subsets of indicator populations based on assumptions of little differentiation within or among proximal populations. In the current study of southern BC coho salmon (Oncorhynchus kisutch) populations, parentage-based tagging (PBT) analysis provided novel information on migration and life history patterns to test the assumptions of biological homogeneity over limited (generally < 100km) geographic distances and, potentially, to inform management of fisheries and hatchery broodstocks. Heterogeneity for location and timing of fishery captures, family productivity, and exploitation rate was observed over small geographic scales, within regions that are, or might be expected to be, within the area encompassed by a single tagged indicator population. These results provide little support for the suggestion that information gained from tagged indicator populations is representative of marine distribution, productivity, and exploitation patterns of proximal populations.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".