Introduction to the taxonomy of the amphibians of Kaieteur National Park, Guyana
Bibliographic record
Abstract
Guyana's Kaieteur National Park (KNP) is centered around the spectacular Kaieteur Falls.Located in the eastern foothills of the Pakaraima Mountains, the park encompasses a variety of habitats which support important biodiversity.Very few biological inventories of the Pakaraima region have been undertaken.This book represents the first intensive inventory of the herpetofauna of an important location in the region.This book is the culmination of numerous visits to KNP, over several years, by the authors.The book is useful as both a field guide to amphibians of KNP and as a "how to" book on herpetological taxonomy and surveys.The book begins with a 16-page description of the park area and its varied habitats.This is followed by an extensive introduction to amphibian orders and families.The next section, 30 pages in length, covers the planning, organization and execution of a field study.Subjects include living in the field, food, collection methods, note taking, photography, call recording, sampling methods, and permit requirements.This is a very detailed and useful resource; although it is written with respect to Guyana and KNP, it will be applicable to other locations.Information of this type is very valuable because it encourages comparability of results by advocating standardized methods of collection and documentation.The systematic section of the book commences on page 61.It begins with a
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.015 |
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".