Let your data run free? The challenge of data redaction in paleontological collections
Bibliographic record
Abstract
Abstract The data associated with museum collections are essential components for many types of analysis. Geodetic information, such as latitude and longitude, in particular, are necessary in the analysis of paleontological data sets when investigating the effect of climate change on biodiversity. However, the release of geodetic information can be problematic when those data are used to deplete paleontological resources. This problem and others (such as overcollecting and impact on sensitive ecosystems) are discussed in all disciplines of natural history. Herein we discuss the problem as it pertains to paleontological collections, with particular reference to collections and digitization objectives in the USA, discuss strategies employed for data redaction, and make the case for allowing almost all locality data to be released in the interest of science and an obligation to the public.
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 | 0.415 | 0.509 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.017 | 0.022 |
| Scholarly communication | 0.030 | 0.038 |
| Open science | 0.011 | 0.020 |
| Research integrity | 0.013 | 0.021 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".