MODELING SPECIES ABUNDANCE WITH IMPERFECT DETECTION USING ANGLING
Bibliographic record
Abstract
Modeling species abundance and predicting species distribution are important tools for fisheries management. Advanced modeling techniques have allowed for predictive models with increased accuracy to be developed because of their ability to account for imperfect detection. Novel sampling methods have been created to sample aquatic environments such as large bodies of water that cannot be easily sampled by traditional fisheries methods. I surveyed Loughborough Lake near Kingston, Ontario to develop abundance models that would predict species distribution based on depth and fetch habitat variables using angling as the sampling method. Loughborough Lake is a 2000-hectare lake that has interesting bathymetric and limnological characteristics with two distinct basins. An angling protocol was developed to be able to sample all environments of the lake while being able to efficiently sample sites. Abundance modelling was completed for Largemouth Bass, Smallmouth Bass, Rock Bass, Pumpkinseed, Bluegill and Yellow Perch. Results showed that a broad distribution of centrarchid species, and other species, was not observed throughout Loughborough Lake. In contrast, centrarchid distribution was characterized as basin-specific in nature with different species occupying either the eastern or western basin. This pattern was unexpected, but it may be explained by variation in fetch, which functions as a surrogate for vegetation. Angling was found to be an effective method that allowed an individual sampler to efficiently obtain count data. The valuable information gained from the combination of the abundance models and a novel sampling method could improve sampling programs and aid in future research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".