Females do count: Documenting Chironomidae (Diptera) species diversity using DNA barcoding
Bibliographic record
Abstract
This dataset contains the digitized treatments in Plazi based on the original journal article Hebert, Torbjørn Ekrem Elisabeth Stur Paul D. N. (2010): Females do count: Documenting Chironomidae (Diptera) species diversity using DNA barcoding. Organisms Diversity & Evolution 10 (5): 397-408, DOI: 10.1007/s13127-010-0034-y, URL: http://dx.doi.org/10.1007/s13127-010-0034-yAbstract Because the family Chironomidae, or non-biting midges, is one of the most species-rich groups of macroinvertebrates in freshwater habitats, species-level identifications of chironomids are important for biodiversity assessments in these ecosystems. Morphology-based species identifications from adult female chironomids usually are considerably more difficult than from adult males, or even impossible; thus, the females are often neglected in community assessments. We used DNA barcoding to investigate how inclusion of the females influenced the species count from springs and spring brooks at Sølendet Nature Reserve in Central Norway. By means of the barcodes we were able to identify 77.6% of the females to species by associating them with males from the study site or from other regions, whereas the remaining, unassociated females could be identified to genus level only. The number of recorded species increased by 27% when females were included. We also found that DNA barcoding is effective for the detection of taxonomically challenging species and species groups. Using DNA barcoding in combination with traditional taxonomy, we recognised at least five species new to science and three species and one genus new to Norway.T. Ekrem (Z): E. SturNorwegian University of Science and Technology,Museum of Natural History and Archaeology,NO-7491 Trondheim, Norwaye-mail: Torbjorn.Ekrem@vm.ntnu.noE. Sture-mail: Elisabeth.Stur@vm.ntnu.noP. D. N. HebertBiodiversity Institute of Ontario, University of Guelph,579 Gordon Street,Guelph, Ontario, Canada N1G 2W1e-mail: phebert@uoguelph.ca
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.019 |
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".