Bibliographic record
Abstract
Odontoscirus longirostris (Hermann, 1804) Distribution in Iran – Fars Province: Region not mentioned (Ostovan and Kamali, 1995c). General distribution – Paraguay, Argentina, Brazil, Italy, Ireland, England, Canada (Herschel Island, Yukon Territory, Bering Island, The commander Islands), Alaska, Australia, Northern Bosnia (Balkan caves), Austria, Germany (Wangerooge Island), United States (Iowa, Missouri, Illinois, Indiana, Ohio, Texas, California, Kansas, Arkansas, Florida, Michigan, Montana), Mexico (Michoacán, Jalisco, Oaxaca, Guanajuato, México, Distrito Federal, Puebla, Nuevo León, Guerrero, Tamaulipas), Cuba, Costa Rica, Jamaica, Argentina, Denmark, Japan, Switzerland, Kure Island, Sainte-Hélène Island, Bohemia, Taiwan, Hawaii, Poland, China (Fujian), Iran, Crimea (Canestrini 1886; Berlese 1888; Hull 1915, 1918; Ewing 1917; Banks 1919, 1923; Womersley 1933; Willmann 1941, 1951, 1952; Atyeo 1960, 1977; Ehara 1961; Shiba 1969; Schweizer and Bader 1963; Butler and Usinger 1963; Garret and Haramoto 1967; Lelláková-Duškova 1978; Tseng 1978; Swift and Goff 1987; Michocka 1987; Ostovan and Kamali 1995c; Lin and Zhang 2000; Kamali et al. 2001; Ueckermann et al. 2007. Bednarskaya 2011). Collection place(s) – Stored products (Ostovan and Kamali, 1995c).
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 | 0.000 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".