Results of first morphometric analyses of <i>Bidens</i> (Asteraceae) in British Columbia
Bibliographic record
Abstract
In southwestern British Columbia (BC), three closely related Bidens (Asteraceae) species overlap in habitat preferences and many of their morphological traits, leading to challenges in their identification in herbarium collections and in the field. We conducted a morphometric study of morphological traits commonly used to distinguish the taxonomically challenging species Bidens amplissima, Bidens cernua, and Bidens tripartita, to explore whether this can help distinguish B. amplissima from its morphologically similar, closely related species in BC. We measured traits on 153 herbarium specimens from the University of British Columbia Herbarium, and 83 mature flowering individuals from five localities in southwestern BC. We used linear discriminant analysis (LDA) on herbarium and field data. We found that most individuals could be accurately classified, despite varying degrees of overlap in LDAs for all taxa. Our findings also suggest that several morphological traits commonly described for B. amplissima, B. cernua, and B. tripartita are generally unable to separate these taxa, both in the field and the herbarium. Resolving the taxonomy and relationships of these species is of interest, given that B. amplissima is listed as Special Concern in Canada. Our study highlights the importance of improving accurate identification and data collection for species at risk.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".