Bibliographic record
Abstract
Quesada gigas (Olivier, 1790) Cicada gigas Olivier 1790: 750 (Java) (error). Cicada triupsilon Walker 1850: 103 (Unknown collection locality). Cicada sonans Walker 1850: 104 (Unknown collection locality). Cicada consonans Walker 1850: 106 (West Coast of America). Cicada vibrans Walker 1850: 107 (Unknown collection locality). Tympanoterpes sibilarix Berg 1879: 141 (Argentina, Brasil, Bolivia). Distribution. Quesada gigas may have the most extensive north to south range of any cicada species. Specimens have been recorded from as far south as central Argentina, Belize, Bolivia, Brazil, Colombia, Costa Rica, Ecuador, El Salvador, French Guiana, Guatemala, Guyana, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, the Antilles, the West Indies, Trinidad & Tobago, Uruguay, Venezuela, with the northernmost expansion extending into Texas in the southern United States (Metcalf 1963a; Duffels & van der Laan 1985; Sanborn 2011a; 2013; 2014; 2018; 2019a; 2020a, c, d, e, 2023b; Sanborn & Heath 2014; Nunes et al. 2023). Canada (Sanborn & Heath 2017), Suriname (Sanborn 2020d), and Chile (Sanborn 2021b) are the only countries in the continental New World without a record of the species. Ruffinelli (1970) reported the species from Yacaré, Salto, and Montevideo in Uruguay.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 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".