Conservation Paleobiology: Leveraging Knowledge of the Past to Inform Conservation and Restoration
Bibliographic record
Abstract
Humans now play a major role in altering Earth and its biota. Finding ways to ameliorate human impacts on biodiversity and to sustain and restore the ecosystem services on which we depend is a grand scientific and societal challenge. Conservation paleobiology is an emerging discipline that uses geohistorical data to meet these challenges by developing and testing models of how biota respond to environmental stressors. Here we (a) describe how the discipline has already provided insights about biotic responses to key environmental stressors, (b) outline research aimed at disentangling the effects of multiple stressors, (c) provide examples of deliverables for managers and policy makers, and (d) identify methodological advances in geohistorical analysis that will foster the next major breakthroughs in conservation outcomes. We highlight cases for which exclusive reliance on observations of living biota may lead researchers to erroneous conclusions about the nature and magnitude of biotic change, vulnerability, and resilience.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.009 | 0.025 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".