Bibliographic record
Abstract
Abstract The critical agenda for mammalian ecologists over this century is to obtain a synthetic and predictive understanding of the factors that limit the distribution and abundance of mammals on Earth. During the last 100 years, a start has been made on this agenda, but only a start. Most mammal species have been described, but there still are tropical areas of undisclosed species richness. We have been measuring changes in distribution and abundance of many common mammals during the last century, and this monitoring agenda has become more critical as climate change has accelerated and habitat destruction has increased with human population growth. There are a small number of factors that can limit the distribution and abundance of mammals: weather, predation, food supplies, disease, and social behavior. Weather limits distribution and abundance mostly in an indirect manner by affecting food supplies, disease, and predation in the short term and habitat composition and structure in the longer term. A good starting point for all studies of mammals is to define them within a well-structured trophic web, and then quantify the major linkages within that web. We still are far from having data on enough model systems to develop a complete theory and understanding of how food webs are structured and constrained as climate shifts and humans disturb habitats. We have many of the bits and pieces for some of our major ecosystems but a poor understanding of the links and the resilience of our mammalian communities to changes in trophic webs driven by climate change and human disturbances.
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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 | 0.002 | 0.003 |
| 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 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".