Amazonian forest termites: a species checklist from the State of Acre, Brazil
Bibliographic record
Abstract
Abstract The Brazilian state of Acre is located in the southwestern Amazon and it is characterized by a humid tropical forest vegetation that covers plains and mountains. Up to this point, the composition of termite species in the state is not known. The aim of this study was to provide a checklist of termite species or recognizable taxonomic units for the state of Acre. Sampling was conducted through field expeditions at the Serra do Divisor National Park, Chandless State Park, Humaitá Forest Reserve, and Chico Mendes Environmental Park using a standardized rapid termite inventory protocol in the first two areas and active searching collections in the others, without a specific protocol. This study also included occurrence records published in the scientific literature. A total of 128 species and morphospecies of termites were found in Acre, distributed across 59 genera and four families. The most frequently occurring species in Acre was Heterotermes tenuis (Hagen, 1858). The study also identified six new species records for Brazil. The predominant feeding groups were soil-feeders and wood-feeders, as expected from data obtained from surveys in humid tropical forests. Despite the significant number of new records for Acre (112), it is concluded that a larger sampling effort is still required, as many areas of the state have not yet been studied for termites.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".