Characterization and mapping of sensilla on the head appendages of noterid larvae (Coleoptera: Noteridae), and development of a preliminary biometric method for taxa delimitation
Bibliographic record
Abstract
Larvae of the burrowing water beetle family Noteridae are distributed worldwide and are often abundant in a broad range of aquatic habitats, playing an important role in structuring freshwater communities, yet they have remained among the most poorly studied groups of aquatic beetles. Studies on sensillar equipment of aquatic insect larvae are largely lacking, despite their potential use in phylogeny and biometric identification methods. In this article, the external morphology and distribution of sensilla on the head appendages of first instar larvae of selected genera of Noteridae were examined using scanning electron microscopy. Seven main types were distinguished based on their morphological structure: basiconica (3 subtypes), campaniformia (2 subtypes), chaetica (7 subtypes), coeloconica (6 subtypes), coniform complex (2 subtypes), placodea, and styloconica (3 subtypes). The apex of the labial palpus was found to be the most variable and informative region in regard to the number, relative position, and topology of sensilla. Fingerprint models were, therefore, generated for this region in each of the studied genera, allowing their identification.
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 | 0.000 |
| 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 | 0.000 |
| 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".