Bibliographic record
Abstract
T. vulgaris (Tullberg, 1871) – Macrotoma vulgaris Tullberg, 1871: 149 AK Christiansen & Bellinger 1980, 1998 BC Rusek & Marshall 1995; Cannings & Scudder 2005; Cannings 2010 AB Powell & Skaley 1975, cf.; Skidmore 1995; Lindo 2014 ON James 1933; Brown 1934; Mills 1934; Christiansen 1964; Skidmore 1995 QC Sharma 1964 L Christiansen 1964 General distribution: Holarctic; “the species appears to be widely distributed over the northern two-thirds of the U.S., southern and Eastern Canada ” (Christiansen 1964, p. 666). Unplaceable records of Tomocerus sp. (earlier ones may also be Pogonognathellus): AK Watson et al. 1966 BC Cannings & Cannings 1997; Cannings 2010 AB Powell & Skaley 1975 ON Judd 1963 QC Stainer 1969; Turnbull 2014 NF Arulnayagam 1995
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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".