Predation and refugia: implications for <i>Chaoborus</i> abundance and species composition
Bibliographic record
Abstract
Summary 1. Previous studies have suggested that the occurrence of larval Chaoborus in lakes may be affected by fish predation, pH, elevation, temperature, nutrient level, water transparency and interspecific competition, but so far, a detailed statistical evaluation of these findings has not been performed. 2. The aim of this study was to apply regression and ordination techniques to a large data set of 56 lakes in order to test which variables related to lake morphology, water chemistry, and fish predation determine (1) the abundance of individual Chaoborus species and (2) their species composition. 3. Individual Chaoborus species were influenced by very different sets of environmental factors. Nutrient levels positively affected the largest species, Chaoborus americanus, which was restricted to fishless lakes. Abundance of the smallest and most transparent species, C. punctipennis, seemed to be controlled more by the larger Chaoborus species than by fish. Larger chaoborids required low water clarity in order to co‐exist with fish, probably to increase refuge availability. Generally, small lakes (for C. flavicans/C. trivittatus) and shallow lakes (for C. punctipennis) supported higher abundances of Chaoborus.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".