Elliptical <scp>T</scp>ime‐<scp>D</scp>ensity model to estimate wildlife utilization distributions
Bibliographic record
Abstract
Summary We present a new animal space‐use model (elliptical time density – ETD ) that uses discrete‐time tracking data collected in wildlife movement studies. The ETD model provides a trajectory‐based, nonparametric approach to estimate the utilization distribution ( UD ) of an animal, using model parameters derived directly from the movement behaviour of the species. The model builds on the theory of ‘time‐geography’ whereby elliptical constraining regions are established between temporally adjacent recorded locations. Using a W eibull speed distribution fitted for an animal's movement data, a time density value (i.e. time per unit landscape) is determined from the expectation of all elliptical regions equal to, or greater than, the minimum bounding ellipse for a given landscape point. We tested the ETD model using a tracking dataset for an A frican elephant ( L oxodonta africana ) and compared the resulting UD s for regularly sampled, frequently recorded locations, as well as irregular random time intervals between locations and also infrequent temporal‐sampling regimes, providing insight to the method's performance with different resolution data. We compared the performance of the ETD model, the B rownian bridge movement model ( BBMM ), the time‐geography density estimator ( TGDE ) and the K ernel D ensity E stimator ( KDE ) by calculating omission/commission errors from the predicted space‐use distribution of each model relative to the true known UD of our elephant test data. The comparison was made for the 10–99% percentile UD model areas. The ETD 90 model (i.e. ETD model parameterized using the 90% percentile value of the W eibull speed distribution) resulted in the fewest errors of commission and omission with regard to locating the true movement path at the 99% percentile UD area. The ETD model provides an improved approach for estimating animal UD s since (i) parameters are derived directly from the tracking data rather than assumed; (ii) parameter values are biologically interpretable; (iii) the W eibull speed distribution is adaptable to various temporal‐sampling regimes; and (iv) the ETD model handles the case of degenerate ellipses thus preserving landscape connectivity in the UD . Software (freeware) for calculating the ETD and a B ayesian framework for estimating the W eibull distribution speed parameters are also introduced in the paper.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".