Some Like It Cold: A General Habitat Association Model for Smallmouth Bass in Stratified Lakes
Bibliographic record
Abstract
ABSTRACT Models describing associations between fish distribution and environmental or spatial gradients at the population level have the potential to be transferrable if model parameters are stationary among populations and over years. Further, population‐level habitat association models represent the scale of effect—habitat relevant to within‐population distribution and processes. Here we show that for a widely recognised warm water fish species (smallmouth bass; Micropterus dolomieu Lacepède, 1802), habitat use extends into the metalimnion and hypolimnion of lakes. Lake depth at net sites and temperature at capture depth were used to model habitat use in a multi‐lake set ( n = 11 lakes) and for a large lake with three surveys over a decade. In the multi‐lake set, a depth model was top ranked with little difference among lakes. In the lake with multiple surveys over a decade, a quadratic temperature model was top ranked but resulted in among‐year differences in occupancy levels at any given temperature. The second ranked depth model produced consistent occupancy patterns with depth and matched parameter values from the multi‐lake model. This consistency points to a general habitat association model based on depth for smallmouth bass during the summer season. We provide guidance for habitat managers based on this stationarity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".