Size-based dynamics of a demersal fish community: modeling fish-fisheries interactions
Bibliographic record
Abstract
This thesis defends a holistic approach to fish dynamics, supports size as a factor determining functional groups in a community, and presents a model that can serve as a framework for the integration of biological knowledge of fish communities with decision-making about resource exploitation. We discuss the aspects that should be considered to approach the study of fish species dynamics. In their natural environment fish species dynamics are influenced by the presence of other species. Interacting species form a community that lies at the core of this thesis. Fishery and survey data show drastic changes in the Newfoundland demersal fish community during the period from the late 70s to the early 90s. We use these changes to analyse size as an indicator of species response to fisheries. We find that size at the community level can substitute for species to determine functional groups that direct community dynamics. This size-based approach shows properties of the community that cannot be explained by looking at each single species one at a time. Thus, a size-based simulation model is built to analyse long-term community dynamics and its response to fisheries. The model has only three simple assumptions: (1) fish pass through a series of age-determined size classes through their life history, (2) big fish eat little fish, and (3) predation cannot drive species to extinction. The model is stable over runs of centuries, and from a stabilized state can be used to explore several scenarios involving environmental and fishery disturbances.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".