Data from: Habitat-based polymorphism is common in stream fishes
Bibliographic record
Abstract
1. Morphological differences (size and shape) across habitats are common in lake fish where differences relate to two dominant contrasting habitats: the pelagic and littoral habitat. Repeated occurrence of littoral and pelagic morphs across multiple populations of several lake fish species has been considered as important evidence that polymorphism is adaptive in these systems. It has been suggested that these habitat-based polymorphic differences are due to the temporal stability of the differences between littoral and pelagic habitats. 2. Although streams are spatially heterogeneous, they are also more temporally dynamic than lakes and it is still an open question whether streams provide the environmental conditions that promote habitat-based polymorphism. We tested whether fish from riffle, run and pool habitats, respectively, differed consistently in their morphology. 3. Our test compared patterns of morphological variation (size and shape) in 10 fish species from the three stream habitat types in 36 separate streams distributed across three watersheds. 4. For most species, body size and shape (after controlling for body size) differed across riffle, run and pool habitats. Unlike many lake species, the nature of these differences was not consistent across species, possibly because these species use these habitat types in different ways. 5. Our results suggest that habitat-based polymorphism is an important feature also in stream-fishes despite the fact that streams are temporally variable in contrast to lake systems. Future research is required to assess whether the patterns of habitat-polymorphism encountered in streams have a genetic basis or they are simply the result of within generation phenotypic plasticity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.048 | 0.012 |
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".