Extending mammal specimens with their essential phenotypic traits
Bibliographic record
Abstract
Natural history collections are repositories of biodiversity specimens that provide critical infrastructure for studies of mammals. Over the past 3 decades, digitization of collections has opened up the temporal and spatial properties of specimens, stimulating new data sharing, use, and training across the biodiversity sciences. These digital records are the cornerstones of an "extended specimen network," in which the diverse data derived from specimens become digital, linked, and openly accessible for science and policy. However, still missing from most digital occurrences of mammals are their morphological, reproductive, and life-history traits. Unlocking this information will advance mammalogy, establish richer faunal baselines in an era of rapid environmental change, and contextualize other types of specimen-derived information toward new knowledge and discovery. Here, we present the Ranges Digitization Network (Ranges), a community effort to digitize specimen-level traits from all terrestrial mammals of western North America, append them to digital records, publish them openly in community repositories, and make them interoperable with complimentary data streams. Ranges is a consortium of 23 institutions with an initial focus on non-marine mammal species (both native and introduced) occurring in western Canada, the western United States, and Mexico. The project will establish trait data standards and informatics workflows that can be extended to other regions, taxa, and traits. Reconnecting mammalogists, museum professionals, and researchers for a new era of collections digitization will catalyze advances in mammalogy and create a community-curated trait resource for training and engagement with global conservation initiatives.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".