A production estimate approach to analyze habitat and weather effects on recruitment of two endangered freshwater fish
Bibliographic record
Abstract
Factors affecting the early life survival of fishes are often difficult to demonstrate because variable immigration and mortality rates coupled with noncontinuous sampling may confound estimates of mortality and bias inference to more numerous smaller individuals. The larval production estimate (LPE) method eliminates these problems by compensating catch data for size- or age-specific mortality and growth and back-calculating abundance at a predetermined size or age. Despite its utility, LPE has not been widely applied in studies of freshwater fish recruitment. We executed an LPE analysis using 10–14 mm and 15–19 mm size classes of Upper Klamath Lake’s (UKL) endangered Lost River suckers ( Deltistes luxatus ) and shortnose suckers ( Chasmistes brevirostris ) for five cohorts per year for 1995–2001. Larval survival peaked when habitat conditions included high availability of emergent macrophytes as habitat (>15 000 m3), air temperatures between 14 and 22 °C, and a low frequency of wind speeds >16 km·h–1. Age-0 juvenile suckers collected later in each year corroborated results of the LPE analysis, as most (88%) juveniles had otolith-estimated swim-up dates corresponding to early life rearing under the specified habitat conditions. Our results support the management practice of maintaining higher than natural UKL water surface elevations through the larval rearing period.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".