"Complexity: The Evolution of Earth’s Biodiversity and the Future of Humanity" by William C. Burger, 2016. [book review]
Bibliographic record
Abstract
how ironic the anti-Fed attitude of many western ranchers is, given that they directly benefit from government programs (p.40), not to mention that their rural lifestyle is often subsidized by the US government.But the key to Wolf Land is that Niemeyer points out these absurdities and hypocritical viewpoints but doesn't dwell on them.The backbone of the paperback is Niemeyer's travels to areas where wolves live and how he would get to know those pack members so he could figure out how to get wolves to come to an area of a few square inches, so they would step on a hidden trap and become entangled and eventually captured for research purposes.At the beginning of the book Niemeyer says that the reintroduction of wolves in Yellowstone and Idaho in 1995-1996 changed the course of his life (p.7), from contracted killer for the government to utilizing his skills to help wolves recover.He eventually became top man for Idaho wolf recovery, and during his tenure as the leader of the wolf recovery program, the Idaho wolf population increased but the number of wolves killed by the government and the number of livestock lost to wolves declined.Niemeyer can be credited for much of this success; unfortunately, western politics often got
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".