The NOW Database of Fossil Mammals
Bibliographic record
Abstract
Abstract NOW (New and Old Worlds) is a global database of fossil mammal occurrences, currently containing around 68,000 locality-species entries. The database spans the last 66 million years, with its primary focus on the last 23 million years. Whereas the database contains records from all continents, the main focus and coverage of the database historically has been on Eurasia. The database includes primarily, but not exclusively, terrestrial mammals. It covers a large part of the currently known mammalian fossil record, focusing on classical and actively researched fossil localities. The database is managed in collaboration with an international advisory board of experts. Rather than a static archive, it emphasizes the continuous integration of new knowledge of the community, data curation, and consistency of scientific interpretations. The database records species occurrences at localities worldwide, as well as ecological characteristics of fossil species, geological contexts of localities and more. The NOW database is primarily used for two purposes: (1) queries about occurrences of particular taxa, their characteristics and properties of localities in the spirit of an encyclopedia; and (2) large scale research and quantitative analyses of evolutionary processes, patterns, reconstructing past environments, as well as interpreting evolutionary contexts. The data are fully open, no logging in or community membership is necessary for using the data for any purpose.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.136 | 0.124 |
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".