Bibliographic record
Abstract
Abstract The extensive series of color plates in this field guide includes species, sub-species, and additional variations of the U.S. and Canadian tiger beetles and should enable both amateur and specialist to identify nearly all of the tiger beetles they find, especially when combined with the range maps and descriptive information in the species accounts. However, some species are very similar in color and marking patterns, and in these cases, the use of a taxonomic key which includes more detailed diagnostic characters may be needed. The keys that we use here are based on those developed by Harold Willis and Gary Dunn, but we incorporate some reorganization and modifications that should make it easier to distinguish among the species. We also substitute less technical terminology to make the key more user friendly for the nonspecialist. The diagnostic characters are described below and illustrated by line drawings throughout the key. In several cases, we also rely on diagnostic geographic distribution to separate otherwise similar species. Be aware, however, that a few species, especially those within the same couplet, may be especially difficult to separate because of the considerable amount of individual variation and overlap in elytral maculations and other characters.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.339 | 0.176 |
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".