Pacific Great Blue Herons (<i>Ardea herodias fannini</i>) consume thousands of juvenile salmon (<i>Oncorhynchus</i> spp.)
Bibliographic record
Abstract
An array of predators that consume juvenile salmon (genus Oncorhynchus Suckley, 1861) may account for the poor returns of adult salmon to the Salish Sea. However, the Pacific Great Blue Heron (Ardea herodias fannini Chapman, 1901) is rarely listed among the known salmon predators, despite being regularly seen near salmon streams. Investigating heron predation by scanning nesting sites within 35 km of three British Columbia (Canada) rivers for fecal remains containing passive integrated transponder (PIT) tags implanted in >100 000 juvenile salmon from 2008 to 2018 yielded 1205 tags, representing a minimum annual predation rate of 0.3%–1.3% of all juvenile salmon. Most of this predation (99%) was caused by ∼420 adult Pacific Great Blue Herons from three heronries. Correcting for tags defecated outside of the heronry raised the predation rates to 0.7%–3.2%, and was as high as 6% during a year of low river flow. Predation occurs during chick-rearing in late spring and accounts for 4.1%–8.4% of the Pacific Great Blue Heron chick diet. Smaller salmon smolts were significantly more susceptible to Pacific Great Blue Heron predation than larger conspecifics. The proximity of heronries relative to salmon-bearing rivers is likely a good predictor of Pacific Great Blue Heron predation on local salmon runs, and can be monitored to assess coast-wide effects of Pacific Great Blue Herons on salmon recovery.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".