Studying deer habitat on Anticosti Island, Québec: relating animal occurrences and forest map information
Bibliographic record
Abstract
Forest maps are widely available and can provide an inexpensive way to analyse ecological phenomena at a landscape level. This paper presents a case study whose objective was to help manage the deer herd and associated habitat on Anticosti Island. The selection by deer of winter habitat was analysed spatially using aerial survey data and a forest map produced by interpretation of aerial photographs. Initially, it was demonstrated that the spatial distr ibution of deer across the landscape could not be considered random. Grid cells of two different sizes—500 m×500 m and 1 km×1 km—were extracted from the forest map, and landscape indices thought to be relevant were calculated for each to characterize the key landscape features on which deer select their winter habitat. To do this, the landscape indices were correlated with the number of deer found in grid cells of a given size. It was found that deer preferred areas on which balsam fir was present, and areas in which there was a relatively high concentration of regeneration/dense forest edge. Correlations were better for the larger grid cell size suggesting that deer on Anticosti Island selected habitat based on an area larger than 500 m×500 m. However, it is noted that this result is probably also due in part to the modifiable areal unit problem (MAUP) whereby larger window sizes tend to provide better correlations between two variables. The location of the grid cells within the study area also affected the results slightly.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".