Bibliographic record
Abstract
Gohieria fusca (Oudemans, 1902) Distribution in Iran – East Azerbaijan, Mazandaran, and West Azerbaijan provinces: Region not mentioned (Sepasgozarian 1978a; Shahhosseini and Kamali 1989; Faraji 1993b; Mirfakhraii 1994; Modarres Awal 1994, 1997; Sepasgozarian 1997), Guilan Province: Hashtpar (Noei 2007; Noei and Ostovan 2012). General distribution – Belgium, Canada, China, Czech Republic, Egypt, England, France, Germany, Greece, Ireland, Japan, Netherlands, New Zealand, Poland, Russia, Scotland, Taiwan, Turkestan, United States, and Wales (Hagstrum et al. 2013). Collection place(s) – Barley, bean (red), buckwheat, buckwheat product, Chinese medicine, dried fruit, dried vegetable, fish (dried), flax seed, fungus (dried), grain, grass seed, hemp seed, herb, kaoliang, malt, mushroom, poppy seed, protein poultry feed, rice, rice (husked), soybean sauce, soybean sweeping, sugar (packaged), sugar beet seed, wheat, wheat bran, wheat flour (packaged), wheat pollard, wheat flour sweeping, stored rice, rice dust and debris (Noei 2007; Noei and Ostovan 2012; Hagstrum et al. 2013).
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 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".