L'écologie Spatiale des Relations Prédateur-Proie dans les Lacs
Bibliographic record
Abstract
The pelagic zone of lakes is defined as the water column over the area of the lake benthos that does not receive enough light from the sun to allow macrophytes to grow. The four chapters of this thesis explore the spatial ecology of predator-prey interactions between schooling fish and their fish predators in this featureless environment. We first developed novel hydroacoustics methods to study fish and fish school swimming behaviour in the pelagic zone (Chapter 1 and 2). Then we characterized our in-situ school formation and prey movement observations using an ideal gas model to better understand the mechanisms that lead to fish and school densities during the daytime (Chapter 2 and 3). With this model we estimated the functional relationship between the schooling prey densities and predator encounter rates, and verified with empirical data a counterintuitive relationship that encounter rates decreased as overall prey densities increased (Chapter 3). The encounter rates suggested that predation within the pelagic zone might be greatly influenced by external forces if they provide spatial structure which encourages greater degrees of prey aggregation in predictable locations. In this regard we examined the predator-prey dynamics under wind and found large redistributions of prey and predators under windy conditions leading to greater aggregations in downwind locations. Further, we found that our study fish were larger in lakes that were oriented into the wind, perhaps demonstrating a benefit to fish growth under windy conditions (Chapter 4).
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".