Field Estimate of Net Trophic Transfer Efficiency of PCBs to Lake Michigan Chinook Salmon from Their Prey
Bibliographic record
Abstract
Chinook salmon (Oncorhynchus tshawytscha) has been the predominant piscivore in Lakes Michigan, Huron, and Ontario since the 1970s, and therefore accurate quantification of its energy budget is needed for effective management of Great Lakes fisheries. A new approach of evaluating a fish bioenergetics model in the field involves field estimation of the efficiency with which the fish retains PCBs from its food. We used diet information, PCB determinations in both chinook salmon and their prey, and bioenergetics modeling to generate a field estimate of the efficiency with which Lake Michigan chinook salmon retain PCBs from their food. Our field estimate is the most reliable field estimate to date because (a) the estimate was based on a relatively high number (N = 142) of PCB determinations for chinook salmon from Wisconsin waters of Lake Michigan in 1985, (b) a relatively long time series (1978-1988) of detailed observations on chinook salmon diet in Lake Michigan was available, and (c) the estimate incorporated new information from analyses of chinook salmon age and growth during the 1980s and 1990s in Lake Michigan. We estimated that chinook salmon from Lake Michigan retain 53% of the PCBs that are contained within their food.
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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 | 0.001 |
| 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) | 0.000 | 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".