Bibliographic record
Abstract
I From Philosophy to Science: Why Should We Protect Endangered Species? Philosophical and Ecological Rationale, Karen Beazley Species at Risk in Canada, Bill Freedman, Lindsay Rodger, Peter Ewins, and David M. Green Avoiding the Endangerment of Species - The Importance of Habitats and Ecosystems, David Gauthier and Ed Wiken Endangered Species and Terrestrial Protected Areas, Philip Dearden Endangered Species and Marine Protected Areas, Martin Willison. II From Science to Policy: The Politics of Endangered Species - An Historical Perspective, Stephen Bocking In Search of a Minimum Winning Coalition - The Politics of Species at Risk Legislation in Canada, William Amos, Kathryn Harrison, and George Hoberg Risk Politics in Western States - Canadian Species in Comparative Perspective, Robert Boardman International Initiatives, Commitments and Disappointments - Canada, CITES and the Convention on Biological Diversity, Philippe Le Prestre and Peter Stoett The Prospects for Canada's Species at Risk, Robert Boardman, Amelia Clarke, and Karen Beazley.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".