Atypical Wing Venation in<i>Dialictus</i>and<i>Hemihalictus</i>and Its Implications for Subgeneric Classification of<i>Lasioglossum</i>
Bibliographic record
Abstract
The subgeneric classification of hundreds of species inLasioglossumCurtissensu latois currently unstable due to differing opinions on the suitability of wing venation characters for differentiating subgenera. The subgeneraDialictusRobertson andHemihalictuswere both originally defined primarily by the forewing having two submarginal cells. I present examples of variation in submarginal cell number in the type species of these two subgenera:L. (Dialictus)anomalum(Robertson) andL. (Hemihalictus)lustrans(Cockerell). These results suggest that submarginal cell number is insufficient for recognizing subgenera inLasioglossum. The variability of this character is used to refute the classification proposed by some authors thatChloralictusRobertson, but notDialictus, be synonymised withEvylaeusRobertson.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".