Bibliographic record
Abstract
ping stories over beer and crisps first came to a realization that frogs might be in decline worldwide. One story after an-other of frogs and toads disappearing, getting rarer, or simply not being where they used to be raised no small amount of worry among the participants (Wake 1991). The following year, 1990, in Irvine, California, a hastily convened confer-ence met to ask if there was indeed evidence of extinctions among amphibian populations. Were there genuine losses greater than background extinction rates? Were amphibians bio-indicators of greater ecological disaster? In this climate of crisis, the Declining Amphibian Populations Task Force (DAPTF) was formed under the Species Survival Commission (SSC) of the World Conservation Union (IUCN) in 1991 (Vial and Saylor 1993). That same year, a Canadian working group assembled and called itself Declining Amphibian Populations Canada (DAPCAN; Green 1997a). If amphibians were in decline, what was the cause? Was there some global plague killing them off? Contaminants were implicated: acid precipitation and toxic chemicals can kill eggs and embryos (Berrill et al. 1997, Grant and Licht 1997). UV-b radiation was fingered: there is evidence that some species have greater repair abilities (the enzyme pho-tolyase) than others (Blaustein et al. 1994a). Were there epi-demic diseases caused by chytrid fungus; “redleg ” bacteria infections due to Aeromonas, Pseudomonas, or other pathogens; or viral diseases such as Iridovirus
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".