Species delimitation in <i>Noccaea densiflora</i> species complex (Brassicaceae) based on morphological and molecular data
Bibliographic record
Abstract
The accurate identification of an organism is the basis of all biological disciplines. Although there have been many different species concepts and methods proposed, researchers generally choose the most appropriate concept according to their interests. However, each species concept has both advantages and disadvantages. In such cases, an integrated concept based on evidence obtained from different species concepts and methods is suitable for the accurate delimitation of a species. The biggest dilemma of integrated species delimitation methods is the inconsistency between species concepts and methods. Herein, the congruency of the different concepts and methods was tested to reveal the relationship of the Noccaea densiflora species complex. Nuclear ribosomal internal transcribed spacer, chloroplast trnL-F, and trnQ-5′rps16 regions were used for species delimitation using the multispecies coalescent model as implemented in two developed Bayesian methods. Internal transcribed spacer and trnL-F regions were also used for Templeton, Crandall, and Singh haplotype network analyses. In addition to morphological measurements from different populations and vouchers, specific bioclimatic data values were also used for the principal component analysis. Based on the analyses, it was determined that only one population of N. densiflora (Boiss. & Kotschy) F.K. Mey. and one of Noccaea microstyla (Boiss.) F.K. Mey. were clearly distinct, whereas the rest of the specimens remain taxonomically uncertain.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".