Long-term surveys of age structure in 13 ungulate and one ostrich species in the Serengeti, 1926–2018
Bibliographic record
Abstract
The Serengeti ecosystem spans an extensive network of protected areas in Tanzania, eastern Africa, and a UNESCO Wold Heritage Site. It is home to some of the largest animal migrations on the planet. Here, we describe a dataset consisting of the sample counts of three age classes (infant, juvenile and adult) of 13 ungulate and one ostrich species. Sample counts were tallied visually from the ground, or, in some instances, aerial photographs, during a period extending from 1926 to 2018. Observed animals were assigned to age classes based on specific criteria for each species. For nine of the 14 species of this dataset, the number of sampling years is over 30. This resulted in a total of 533 different records of count across age classes. By computing age-class ratios, these data can be used to measure long-term recruitment success at different ages of the tallied species. In particular, the temporal extent of these data allows comparison of patterns to other long-term processes, such as the El Niño-Southern Oscillation (ENSO).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".