Estimating fish production in wetlands
Bibliographic record
Abstract
Fish production integrates changes in biomass from growth, reproduction, and mortality, and is a useful indicator for fisheries management. However, calculation of fish production has been limited by the intensive requirements for data on abundance, biomass, age structure, and vital rates, and so it is uncommon to find estimates of fish production for wetlands. We developed an approach to directly estimate production that is suitable for data-limited systems: a continuous time model of production describing individual growth over short time intervals with continuous time models of abundance and biomass over longer timescales. We applied this model for 18 Great Lakes coastal wetlands (GLCWs) on Lake Ontario, including Big Island Wetland (BIW). In BIW, most species were dominated in abundance and biomass by younger cohorts and, as a result, these young, fast-growing individuals contributed disproportionately to fish production. In total, BIW produced 336.1 kg·ha·year of fish and the other 17 neighbouring ranged between 447.7 and 1119.9 kg·ha·year. These are some of the first estimates of fish production for GLCWs, highlighting their value for managing Great Lakes fisheries.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".