The importance of prey distribution in habitat use by arctic foxes (<i>Alopex lagopus</i>)
Bibliographic record
Abstract
The use of habitat by reproducing arctic foxes (Alopex lagopus) in relation to prey distribution was examined on the Svalbard archipelago in the Norwegian High Arctic during 19871988 and 19972000 by means of VHF telemetry. The distribution and abundance of the main prey species of foxes was registered in 4 separate periods during summer. The availability of 9 habitat types was estimated on the basis of a classification of a Landsat-5 TM scene. Three resource areas that differed with regard to distribution and availability of prey, vegetation, and terrain were identified within the study area: (1) inland areas with no geese, (2) inland areas with geese present, and (3) coastal areas with bird cliffs. The use of resources by foxes was calculated in the 4 separate periods, as was the average speed of movement (m/h) of foxes and the distance between fox locations and their natal dens. Resource-selection functions (RSFs) calculated for individual animals showed that resource use was nonrandom and similar for foxes that lived within the same resource area. In inland areas in which resource availability was low but fairly stable (area 1), RSFs were simple and in some cases of low significance compared with a no-selection model. In inland areas with highly dynamic resources (area 2), RSFs were complex and resource use differed significantly between periods. In coastal areas (area 3), where resources were plentiful, highly concentrated, and stable, RSFs were of intermediate complexity and resource use differed less between periods.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".