Long‐term monitoring of Serengeti bird species occurrence, abundance, and habitat
Bibliographic record
Abstract
The Serengeti ecosystem contains one of the most diverse bird assemblages in Africa. We present here a data set consisting of abundances of bird species in different habitats of the Serengeti ecosystem over a 87-yr time frame. This data set comprises 66,643 georeferenced occurrences for 568 species from 1929 to 2017. Most records contain feeding location, food source, distribution status, and observation locality. The records originate from three different but complementary methodologies: points, sites, and transects. The point method (bird species records 1929-2017) is based on ad hoc observations and includes rare species or those in special habitats. These points came from published records as well from the research program of A. R. E. Sinclair and colleagues. The site method (1966-2017) is based on structured observations at sites selected to represent specific habitats, and replicated within habitats and over time. At each site, birds were recorded by sight and sound over a radius of 50 m for 10 min. The transect method (1997-2011) is based on road transects covering different areas of the ecosystem. Road transects were traversed using a vehicle with observers travelling at 30 km/h. Bird species were those easily seen from a vehicle out to 50 m either side. As most transects were traversed multiple times, this method provides information on temporal change in abundance for a select set of species. No copyright restrictions apply to the use of this data set other than citing this publication.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".