Book review: Manual of Central American Diptera, Volume 1.
Bibliographic record
Abstract
As an undergraduate biology major in northern Mexico I do remember my fi rst attempts at keying out some aquatic in-sects I had collected for a class project. Th at was in the early 80’s, still some of the manuals for North America (United States and Canada) worked out fairly well. Nonetheless, soon I realized that even for Mexico, close to the United States yet with a strong Neotropical component, insect identifi cation was a diffi cult and somewhat uncertain task. As a Mexican entomologist I have a general perception about well-studied faunas (e.g., western Europe, North America, Australia), but if I had to select a well-known taxon I would probably pick Diptera. And if I had to name a diverse and complex fauna, where species would be hard to identify, most likely I would say South America and the rest of the Neotropics. However, things are changing for us in Latin America, and for good. A solid tra-dition of dipterology in South America is now getting organized to produce a fast-publication journal, Neotropical Diptera (Amorim and Papavero 2008). Th is goes side ZooKeys 52: 65–67 (2010) doi: 10.3897/zookeys.52.541 www.pensoftonline.net/zookeys Copyright Atilano Contreras-Ramos. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.220 | 0.220 |
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".