Approaches for studying fish production: Do river and lake researchers have different perspectives?
Bibliographic record
Abstract
Biased perspectives of fisheries researchers may hinder scientific progress and effective management if limiting factors controlling productivity go unrecognized. We investigated whether river and lake researchers used different approaches when studying salmonid production and whether any differences were ecologically supported. We assessed 564 peer-reviewed papers published between 1966 and 2012 that studied salmonid production or surrogate variables (e.g., abundance, growth, biomass, population) and classified them into five major predictor variable categories: physical habitat, fertility (i.e., nutrients, bottom-up), biotic, temperature, and pollution. The review demonstrated that river researchers primarily analyzed physical habitat (65% of studies) and lake researchers primarily analyzed fertility (45%) and biotic (51%) variables. Nevertheless, understudied variables were often statistically significant predictors of production for lake and river systems and, combined with other evidence, suggests that unjustified a priori assumptions may dictate the choice of independent variables studied. Broader consideration of potential limiting factors on fish production, greater research effort on understudied genera, and increased publication in broadly scoped journals would likely promote integration between lentic and lotic perspectives and improve fisheries management.
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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 | 0.094 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.019 | 0.029 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".