Bibliographic record
Abstract
Estimation of functional diversity in biological communities requires extensive and complete data on numerous functional traits of species or even individuals. When estimating functional diversity at large scales, this fact possesses an issue that may be hard to overcome: for many species, there might not be sufficient data on their functional traits. In such cases, even if there is missing information on functional trait value for one species in a community, this makes the trait impossible to use for the estimation of the functional diversity of a community. On the other hand, there are available datasets on the functional traits of all extant species within certain lineages across the world, but such datasets are often limited to very few functional traits, missing some dimensions of species' ecological niches. In this dataset, I compiled the available data from various sources that describe 23 functional traits of 703 bird species that occur in Canada, the United States, and Mexico. These functional traits include the following: diet type, diurnal and nocturnal feeding, diet items, feeding methods, feeding substrate, nest type, nest substrates, breeding system, chick development at hatching, nest aggregation, clutch size, first breeding age, number of clutches a year, breeding success, adult annual survival, mean biomass, maximum lifespan, hand-wing index, kleptoparasitism, nest parasitism, and the extent of dependency on other species for building a nest.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.027 |
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".