Predation on Chinook Salmon Parr by Hatchery Salmonids and Fallfish in the Salmon River, New York
Bibliographic record
Abstract
Abstract Naturally reproduced Chinook Salmon Oncorhynchus tshawytscha contribute substantially to the fishery in Lake Ontario. The Salmon River, a Lake Ontario tributary in New York, produces the largest numbers of naturally spawned Chinook Salmon, with parr abundance in the river often exceeding 10 million. In the spring of each year, large numbers of hatchery salmonid yearlings—potential predators of Chinook Salmon parr—are released into the Salmon River by the New York State Department of Environmental Conservation. We sought to examine predation on Chinook Salmon parr in the Salmon River during May and June prior to out-migration. Over the 4 years examined (2009–2012), annual consumption of Chinook Salmon parr by hatchery-released yearling steelhead O. mykiss and Coho Salmon O. kisutch ranged from 1.5 to 3.3 million and from 0.4 to 2.1 million, respectively. In 2009, Fallfish Semotilus corporalis were estimated to consume 2.9 million Chinook Salmon parr. Predation was higher in May, when the average TL of Chinook Salmon parr was 44.5 mm, than in June. Fallfish were also important predators of naturally reproduced steelhead subyearlings, consuming an estimated 800,000 steelhead in 2009. Hatchery-released yearling salmonids consumed 13.8–15.3% of the Chinook Salmon parr that were estimated to be present in the Salmon River during 2010–2012. Earlier releases of hatchery salmonid yearlings could reduce the riverine consumption of Chinook Salmon parr by facilitating the out-migration of yearlings prior to Chinook Salmon emergence. Received February 23, 2015; September 29, 2015
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".