Patch selection behaviour in the presence of \nenvironmental constraints
Bibliographic record
Abstract
Habitat selection behaviour is the primary way in which organisms are able to \nregulate encounters with their biotic and abiotic environment. An individual chooses an \narea that best meets their current needs, particularly regarding safety and the presence of \nhigh-quality food. Several physical aspects of the environment can make it difficult for \nindividuals to assess the relative habitat quality of the areas available, thus leading to suboptimal \nhabitat selection. In this thesis, I investigated the way in which two aquatic \nhabitat constraints - obstacles to movement between patches and turbidity - affected the \nability of fish to make optimal patch choices, using threespine stickleback Gasterosteus \naculeatus as a model species. Laboratory experiments showed that when movement \nbetween patches was hindered by increasingly challenging obstacles, groups of \nstickleback did not move as freely between the patches, and thus had greater deviations \nfrom the predictions of the Ideal Free Distribution (IFD). I also demonstrated that, unlike \nother species, stickleback do not use turbid environments to avoid predator detection. A \ntrend was seen towards avoidance of a turbid food patch regardless of risk level, although \nthis was not statistically significant. As expected, the stickleback avoided feeding in the \npresence of a predator regardless of water clarity. Overall, I found that both turbidity and \nmovement constraints can have significant impacts on patch use and distribution in the \nthreespine stickleback. Both turbidity and ease of transit will impact the distribution of \necologically important species like the threespine stickleback, and therefore should be \ntaken into account when studying habitat selection in the wild.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".