Integrating Paleoecological Databases
Bibliographic record
Abstract
Neotoma Consortium Workshop; Madison, Wisconsin, 23–26 September 2010; Paleoecology can contribute much to global change science, as paleontological records provide rich information about species range shifts, changes in vegetation composition and productivity, aquatic and terrestrial ecosystem responses to abrupt climate change, and paleoclimate reconstruction, for example. However, while paleoecology is increasingly a multidisciplinary, multiproxy field focused on biotic responses to global change, most paleo databases focus on single‐proxy groups. The Neotoma Paleoecology Database ( http://www.neotomadb.org ) aims to remedy this limitation by integrating discipline‐specific databases to facilitate cross‐community queries and analyses. In September, Neotoma consortium members and representatives from other databases and data communities met at the University of Wisconsin‐Madison to launch the second development phase of Neotoma. The workshop brought together 54 international specialists, including Neotoma data stewards, users, and developers. Goals for the meeting were fourfold: (1) develop working plans for existing data communities; (2) identify new data types and sources; (3) enhance data access, visualization, and analysis on the Neotoma Web site; and (4) coordinate with other databases and cooperate in tool development and sharing.
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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 | 0.027 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".