Bibliographic record
Abstract
Bryodrilus librus (Nielsen & Christensen, 1959) (T) Marionina libra Nielsen & Christensen, 1959. Bryodrilus librus Schmelz & Collado 2010; Shen 2012; Dózsa-Farkas 2019. Bryodrilus parvus Nurminen 1970; Dash 1970; Xie et al. 2000b; Chen 2009; Schmelz & Collado 2010. For a complete list of synonymies see Dózsa-Farkas et al. (2012). Distribution and habitat. Canada (Dash 1970), Northern and Central Europe (Christensen & Dózsa-Farkas 1999; Schmelz & Collado 2010; Dózsa-Farkas et al. 2019), Siberia Arctic Archipelago (Christensen & Dózsa-Farkas 2006); Russian Far East (Degtyarev et al. 2020). Terrestrial. Distribution in China. Jilin Province (Xie et al 2000b; Chen 2009; Lian et al. 2011; Shen 2012). New records. Sichuan Province, alpine meadows of Zheduo Mountain, 30°04′11.90’N, 101°48′13.46’E, 4344 m asl, leg. X. Jiang, 05. 2019.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".