Ghost runs: management and status assessment of Pacific salmon (Oncorhynchus spp.) returning to British Columbia’s central and north coasts
Bibliographic record
Abstract
The management of Pacific salmon ( Oncorhynchus spp.) populations, which are spatially distributed across thousands of waterways in coastal British Columbia, Canada, presents considerable challenges to resource managers. We evaluated the efficacy of salmon management by Fisheries and Oceans Canada (DFO) over the past 55 years in two key areas: (i) the achievement of internally generated target escapement levels and (ii) escapement monitoring. We show that less than 4% of monitored streams (n = 7 of 215), which represent a small fraction of all salmon-bearing waterways (n = 2592), have consistently met escapement targets since 1950. During this same period, the number of streams monitored by DFO has simultaneously decreased. Further, current monitoring efforts fall short of encompassing the range of salmon diversity identified within recently designated conservation units. Importantly, we found that this erosion of monitoring effort has been biased towards dropping smaller runs that failed to meet target escapements in the previous decade. We suggest that such increasingly selective monitoring is presenting a progressively more biased evaluation of population health. In addition to fostering a “shifting baseline” syndrome, we conclude that these changes to monitoring can not provide data required for precautionary harvest management under the high exploitation levels that these runs experience.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".