Bibliographic record
Abstract
Improving knowledge on threatened species must be done in a manner that does not compromise individuals, and hence populations. The use of noninvasive techniques has great potential in the study and conservation of endangered species. With this end in mind, Evans and Yablokov have written Noninvasive Study of Mammalian Populations. The book is mostly an English-language expansion and revision of their 1983 Russian monograph on cetacean color patterns (Evans and Yablokov 1983). Their approach was motivated by a need to develop less intensive, and nonlethal, ways to study endangered species. The authors have researched cetacean color pattern since the early 1970s. However, noninvasive identification techniques were certainly not new at the time, because cattle breeders have used nose prints to identify individual bulls and their offspring since antiquity (Preface, p. 7). The authors use this book to describe the “phenetics approach” (Timofeev-Ressovsky and Yablokov 1973) as a noninvasive way to study mammal populations. This approach was developed in Russia based on the oldest genetic conception of single characters. A phene may be morphological, physiological, or behavioral, and is “any discreet phenotypic character, which reflects the genotype of an individual and, by its frequency, reflects the genotypic composition of the population” (p. 15).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".