Analyzing Animal Movement Characteristics From Location Data
Bibliographic record
Abstract
Abstract When individuals of a species utilize an environment, they generate movement patterns at a variety of spatial and temporal scales. Field observations coupled with location technologies (e.g.GPStags) enable the capture of detailed spatio‐temporal data regarding these movement patterns. These patterns contain information about species‐specific preferences regarding individual decision‐making, locational choices and the characteristics of the habitat in which the animal resides. Spatial Data Mining approaches can be used to extract repeated spatio‐temporal patterns and additional habitat preferences hidden within large spatially explicit movement datasets. We describe a method to determine the periodicity and directionality in movement exhibited by a migratory bird species. Results using a High Arctic‐nestingSvalbardBarnacleGoose movement data yielded undetected patterns that were secondarily corroborated with expert field knowledge. Individual revisits by the geese to specific locations in the breeding and wintering grounds ofSvalbard,Norway andSolway,Scotland, occurred with a periodicity of 334 days . Further, the orientation of this movement was detected to be mostly north‐south. During long‐range migration the geese use the north‐south orientedNorwegian islands as “stepping stones”, Short‐range movement between mudbank roosts to feeding fields inSolway also retained a north‐south orientation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".