Bibliographic record
Abstract
This issue of the UMKC Law Review is dedicated to Professor Fred Cheever who lived an authentic life in the law and in nature, exemplifying commitment to learning about our natural environment and to protecting it though law and advocacy. Professor Cheever began his legal career with the Sierra Club Legal Defense Fund, which later became Earth justice. Professor of Law at the University of Denver’s Sturm College of Law since 1993, Cheever was the primary organizer of a conference for natural resources law professors through the Rocky Mountain Mineral Law Foundation. After returning from a gathering of this organization of law professors in Banff, Alberta last summer, Fred died while rafting with his family on the Green River in Dinosaur National Monument. Fred would have presented and written on the topic of “The Public Interest in Private Land Conservation” for this symposium. At this symposium gathering and at many more, we remember his kindness and seek to emulate his dedication to the law, to education, and to sustaining our planet.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.021 | 0.015 |
| Insufficient payload (model declined to judge) | 0.012 | 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".