PREDICTIVE MODELS OF MOVEMENT BY SERENGETI GRAZERS
Bibliographic record
Abstract
Many animal species are unevenly distributed across the landscape, in spatial patterns that continually shift over time. Such a shifting mosaic is thought to have profound implications for the persistence and stability of ecosystems. Management and conservation of natural systems would be enhanced if we could accurately predict movement. Such prediction has not yet been possible. Here we use an extensive set of field data on food abundance and quality, combined with experimentally derived measures of nutritional value, to predict the spatial distribution of Thomson's gazelles (Gazella thomsoni thomsoni Gunter) on the Serengeti Plains of East Africa. Twelve plausible models, based on alternate foraging objectives or movement rules, were assessed against field data on food and grazer abundance gathered at biweekly intervals (every two weeks) over the course of the wet seasons in two different years. Nomadic movements of gazelles closely tracked changes in the spatial distribution of short grass swards. Gazelles left short grass patches when local daily energy intake dropped below the expected intake averaged across the landscape. Subsequent redistribution of gazelles among neighboring patches was proportional to daily rates of energy intake in each patch. Thus, nomadic movements by Thomson's gazelles were predictable on the basis of local energy gain. This suggests that adaptive behavioral models can provide useful predictive tools for understanding the dynamics of complex natural systems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".