Running the trophic gauntlet: Empirical support for reduced foraging success in juvenile salmon in tidally mixed coastal waters
Bibliographic record
Abstract
Abstract The productivity of Fraser River sockeye salmon has declined in recent years, with 2019 being the lowest return on record. The cause of the decline is still not fully understood; however, bottom‐up drivers and trophic interactions during the early marine migration are considered to be important contributing factors. McKinnell et al. (Fisheries Oceanography, 23, 2014 and 322) developed a “trophic gauntlet hypothesis,” proposing that low biological productivity leads to an energy deficit from poor foraging opportunities in migrating salmon. When combined with poor foraging conditions in typically productive waters elsewhere on the migration, low marine survival may result. Our study examined prey availability and stomach fullness of juvenile sockeye salmon along the 120 km stretch of the coastal migration through the Discovery Islands and Johnstone Strait to determine whether this section of the migration is indeed food limited. We observed low stomach fullness throughout tidally mixed waters, providing empirical support for the trophic gauntlet hypothesis. Zooplankton abundance was high in these regions so it appeared that unfavourably small prey size may have been the cause of low foraging success. We also observed foraging hotspots at both ends of the gauntlet, indicating that such areas may be key feeding grounds for migratory salmon.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".