Assessing Southern Flying Squirrel, Glaucomys volans, habitat selection with kernel home range estimation and GIS
Bibliographic record
Abstract
Information on habitat selection behavior is vital to effective conservation and management of native terrestrial fauna, particularly in disturbed, fragmented habitats.Application of the kernel probability density estimation method to the description of animal home ranges, coupled with the mapping and analytical capabilities available in geographic information systems, allow researchers to gain a degree of insight into species' habitat use that has not previously been possible.This paper provides examples of habitat selection analyses performed using home range contours produced in the program KERNELHR and input into the Geographic Resources Analysis Support System (GRASS) where home range contours were overlain on habitat maps.Data for the examples were taken from a study of the Southern Flying Squirrel (Glaucomys volans) in fragmented forests in Arkansas, where flying squirrels were tracked by radiotelemetry on five study areas during spring and summer 1994-1996.However, the methods described here may also be applied similarly to other terrestrial vertebrates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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 teacher head, 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".