Estimating spatial mixing within the St. Lawrence Estuary beluga population by comparing local individual diversity and abundance
Bibliographic record
Abstract
Abstract Interindividual variability in habitat preferences affect local abundance and residency times. Within a population range, this can lead to sectors having a continuous flow of unique individuals, with others being used by the same set of resident individuals. These patterns of habitat use by individuals, referred to here as individual spatial mixing, can have important implications for ecological and evolutionary processes. This study proposes a novel approach for estimating the degree of individual spatial mixing in an endangered beluga population, based on the comparison of local individual diversity obtained from photo‐identification data against abundance indices derived from systematic aerial surveys. Divergences between long‐term indices of abundance and diversity were observed across many sectors within the population's summer habitat. This was the case notably for the Saguenay Fjord, where on average only 1.8% of the total population count was detected during summer aerial surveys, but where 41% of all individuals identified in the photo‐identification data were found at least once. The comparative approach proposed in this study to estimate individual spatial mixing can help quantify site fidelity patterns in wildlife populations and estimate its vulnerability to local stressors such as anthropogenic noise.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".