Potential contact statistics for measuring scale-dependent spatial pattern and association: an example of northern cod (<i>Gadus morhua</i>) and capelin (<i>Mallotus villosus</i>)
Bibliographic record
Abstract
Analysis of simulated data showed that potential contact statistics could be used to describe spatial pattern in sample density data. Potential contact is a new method, analogous to Ripley's K function for mapped point pattern analysis. Potential contact can be used to describe spatial pattern and association over a range of scales without grouping data and is robust against the presence of zeros. The statistical output is ecologically interpretable, as a measure of the degree of contact between individuals. This new technique was applied to examine changes in the spatial distribution of Atlantic cod (Gadus morhua) off Newfoundland, Canada, from 1985 to 1994, a period that encompassed a collapse of the cod stock. Sample data from bottom-trawl surveys indicated that cod were aggregated in patches with dimensions of 100-250 km. During the period of cod decline in the 1990s, spatial structure changed in three ways: the number of patches decreased, patch size shrank, and contact with conspecifics at small (10-20 km) scales fell. Cod were broadly associated with capelin (Mallotus villosus), a major prey species. Spatial distribution of capelin changed over the same time period as changes in cod distribution, and there was no evidence that contact between cod and capelin decreased.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".