Madagascar Terrestrial Camera Survey Database 2021: A collation of protected forest camera surveys from 2007–2021
Bibliographic record
Abstract
Madagascar is a threatened global biodiversity hotspot and conservation priority, yet we lack broad-scale surveys to assess biodiversity across space and time. To fill this gap, we collated camera trap surveys, capturing species occurrences within Madagascar into a single standardized database. This data set includes nine distinct protected areas of Madagascar and encompasses 13 subprojects, 38 camera arrays, and 1156 sampling units (independent camera site per survey) within two important biodiversity eco-regions: western dry deciduous forest and eastern humid rainforest. Camera surveys were conducted from June 2007 to January 2021. The final data set includes 17 unique families of mammals (Bovidae, Canidae, Cheirogaleidae, Daubentoniidae, Equidae, Eupleridae, Felidae, Hominidae, Indriidae, Lemuridae, Lepilemuridae, Muridae, Nesomyidae, Pteropodidae, Soricidae, Suidae, Tenrecidae) comprising 45 species and 27 unique families of birds (Accipitridae, Acrocephalidae, Alcedinidae, Bernieridae, Brachypteraciidae, Caprimulgidae, Cisticolidae, Columbidae, Coraciidae, Corvidae, Cuculidae, Dicruridae, Mesitornithidae, Monarchidae, Motacillidae, Muscicapidae, Numididae, Phasianidae, Rallidae, Sarothruridae, Strigidae, Sturnidae, Sulidae, Threskiornithidae, Upupidae, Vangidae, Zosteropidae) comprising 58 species. Images were processed and verified by individual project data set creators and camera operation and species tables were then collated. The final product represents the first broad-scale freely available standardized formal faunal database for Madagascar. Data are available through this publication and at DOI: 10.5281/zenodo.5801806. These data will be useful for examining species-level and community-level trends in occurrence across space or time within Madagascar and globally, evaluating native and invasive species dynamics, and will aid in determining species conservation status and planning for at-risk species. There are no copyright restrictions; please cite this paper when using the data for 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".