Spatially Explicit Habitat Characterization, Suitability Analysis, Verification, and Modelling of the Yellow Perch Perca f1avescens (Mitchell 1814) Population in Long Point Bay, Lake Erie
Bibliographic record
Abstract
Different approaches were used to characterize, assess, test and model the fish-habitat interactions of yellow perch in Long Point Bay. Chapter 1 describes the methodologies for explicitly characterizing spatial and temporal habitat through mapping and modelling. Chapter 2 connects habitat and ontogenetic niche shifts in perch life history, with the aim of determining suitable habitat availability for the Long Point Bay perch population. Habitat suitability indices and models were used to map and identify the areas of suitable habitat, including thermal habitat. Chapter 3 compares a known distribution of yellow perch larvae with HSI predictions of habitat suitability as a validation exercise. Abundance and size distributions from the survey were compared to thermal and HSI predictions of suitable habitat to test for correspondence. The relationship between food availability and habitat characteristics, especially vegetation, were also tested. A model was developed in Chapter 4 that concentrated on the first year of life and the effect of consecutive constraints on early life stages with different habitat requirements. The purpose of the model was to compare the potential growth and survival of consecutive life stages in a spatially explicit manner when different habitat-based rules are imposed. The results highlight the importance of life history theory and knowledge of mechanisms used in habitat selection for determining limits to fish production.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".