An integrative taxonomy approach unveils unknown and threatened moth species in Amazonian rainforest fragments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This study focuses on the importance in hyperdiverse regions, such as the Amazonian forest, of accelerating and optimising the census of invertebrate communities. We carried out low‐intensity sampling of tropical moth (Lepidoptera) assemblages in disturbed forest fragments in Brazil. We combined DNA barcoding and taxonomists’ expertise to produce fast and accurate surveys of local diversity, including the recognition and census of undescribed and endemic species. Integrating expert knowledge of species distributions, we show that despite limited sampling effort, our approach revealed an unexpectedly high number of new and endemic species in severely threatened tropical forest fragments. These results highlight the risk of silent centinelan extinctions and emphasise the urgent need for accelerated invertebrate surveys in high‐endemism and human‐impacted tropical forests.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it