Recruitment of burbot (<i>Lota lota</i> L.) in Lake Erie: an empirical modelling approach
Bibliographic record
Abstract
Stapanian MA, Witzel LD, Cook A. Recruitment of burbot (Lota lota L.) in Lake Erie: an empirical modelling approach. Ecology of Freshwater Fish 2010: 19: 326–337. Published 2010. This article is a US Government work and is in the public domain in the USA Abstract – World‐wide, many burbot Lota lota (L.) populations have been extirpated or are otherwise in need of conservation measures. By contrast, burbot made a dramatic recovery in Lake Erie during 1993–2001 but declined during 2002–2007, due in part to a sharp decrease in recruitment. We used Akaike’s Information Criterion to evaluate 129 linear regression models that included all combinations of one to seven ecological indices as predictors of burbot recruitment. Two models were substantially supported by the data: (i) the number of days in which water temperatures were within optimal ranges for burbot spawning and development combined with biomass of yearling and older (YAO) yellow perch Perca flavescens (Mitchill); and (ii) biomass of YAO yellow perch. Warmer winter water temperatures and increases in yellow perch biomass were associated with decreases in burbot recruitment. Continued warm winter water temperatures could result in declines in burbot recruitment, particularly in the southern part of the species’ range.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".