Functional Traits of the World's Late Quaternary Terrestrial Mammalian Predators
Bibliographic record
Abstract
ABSTRACT Motivation Terrestrial predators play key roles in cycling nutrients, as well as limiting prey populations, and shaping the behaviour of their prey. Prehistoric, historic and ongoing declines of the world's predators have reshaped terrestrial ecosystems and are a topic of conservation concern. However, the availability of ecologically relevant predator functional traits is limited, hampering efforts to understand macroecological changes in this ecologically important functional group. Here, we present CarniTraits, a comprehensive open‐access functional trait database of all late Quaternary (~130,000 ybp) terrestrial mammalian predators (149 species, ≥1 kg body mass, ≥50% vertebrate meat consumption). Main Types of Variables Contained Mammalian terrestrial predator functional traits including body mass, diet, scavenging, locomotion, cooperative hunting, hunting habitat, hunting method, bone consumption, temporal activity patterns, brain mass and encephalisation quotient. Spatial Location and Grain Global. Time Period and Grain Late Quaternary (the last ~130,000 years). Major Taxa and Level of Measurement All late Quaternary terrestrial mammalian predators (149 species, ≥1 kg body mass, ≥50% vertebrate meat consumption). Software Format csv.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".