Bibliographic record
Abstract
Scrobipalpa acuminatella (Sircom, 1850) Gelechia acuminatella Sircom 1850: lxxii. Gelechia pulliginella Sircom, 1850: lxxii. Gelechia cirsiella Stainton, 1851: 4. Lita porcella Heinemann, 1870: 253. Lita ingloriella Heinemann, 1870: 259. Gelechia gracilella Stainton, 1871: 97. Material examined. 1 ♂, Russia, Krasnojarskiy kraі, 53°08’N 92°53’E, 375 m, Tanzybei forest station, Betula / Populus /meadow, 2.vi.1995 (Jalava & Kullberg) (gen. slide 329/16, OB) (MZH); 2 ♂, Russia, Altai Mts, 50°14- 16’N 87°50-55’E, Kuraiskaja step, 1500–1700 m, 5.vii.2001 (Nupponen) (gen. slide 179/16, OB) (NUPP); 2 ♂, Russia, Altai Republic, Chagan-Uzun env., Krasnaya Gorka hill, 50°05´00´´N, 88°25´15´´E, rocky steppe, 1870 m, 1–3.vii.2019 (Šumpich) (Barcode NMPC-LEP-1102, NMPC-LEP-1068) (NMPC); 1 ♂, USSR, Irkutskaja obl., Sljudjanka 50 km E, river Hara-Murin, Betula bush ad. luc., 8–11.vii.1984 (Mikkola & Viitasaari) (gen. slide 245/16, OB) (MZH). Molecular data. BIN: BOLD:AAC1644. The intraspecific average distance of the barcode region is 0.4% (n=154). The minimum distance to the nearest neighbour, an unidentified species of Scrobipalpa from Canada (BIN: BOLD:AAG9134), is 6.93 %. Distribution. Europe; North Iran; Afghanistan; West Kazakhstan; Russia: European part, Novosibirsk region, Altai (new record), Kemerovo region, south of Krasnoyarskiy krai (new record), Irkutsk region; eastern China (Bidzilya 2009: 5; Bidzilya & Li 2010: 2; Huemer & Karsholt 2010: 62).
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 | 0.001 |
| 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".