Allowing for redundancy and environmental effects in estimates of home range utilization distributions
Bibliographic record
Abstract
Abstract Real location data for radio tagged animals can be challenging to analyze. They can be somewhat redundant, since successive observations of an animal slowly wandering through its environment may well show very similar locations. The data set can possess trends over time or be irregularly timed, and they can report locations in environments with features that should be incorporated to some degree. Also, the periods of observation may be too short to provide reliable estimates of characteristics such as inter‐observation correlation levels that can be used in conventional time‐series analyses. Moreover, stationarity (in the sense of the data being generated by a source that provides observations of constant mean, variance and correlation structure) may not be present. This article considers an adaptation of the kernel density estimator for estimating home ranges, an adaptation which allows for these various complications and which works well in the absence of exact (or precise) information about correlation structure and parameters. Modifications to allow for irregularly timed observations, non‐stationarity and heterogeneous environments are discussed and illustrated. Copyright © 2004 John Wiley & Sons, Ltd.
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 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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".