Development of a new field-based approach for estimating consumption rates of fishes and comparison with a bioenergetics model for lingcod (Ophiodon elongatus)
Bibliographic record
Abstract
Development of quantitative tools to characterize the nature of predator–prey interactions is an essential component of the science supporting ecosystem-based management and conservation. A common constraint is our capacity to estimate the feeding rates of fishes at the temporal and spatial scales at which predation occurs. This study developed a new field-based modeling approach for estimating consumption rates of large predatory fishes that requires fewer assumptions and may be more flexible than other field-based methods. We compared the field-based model results with consumption estimates from a bioenergetics model for lingcod ( Ophiodon elongatus ), a top predator in nearshore rocky habitats along the west coast of North America. The models were used to determine population-level consumption of rockfishes ( Sebastes spp.) by lingcod in marine reserves and nonreserve areas in the San Juan Channel, Washington, USA. Based on these models, rockfish consumption by lingcod may have been 5–10 times greater in marine reserves than in nonreserves during fall and summer, 2005–2007. Understanding whether lingcod predation may limit the efficacy of marine reserves for rockfish recovery requires site-specific information on the abundance and size structure of lingcod and rockfishes.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".