Distribution of morphological diversity within widespread Australian species of Poa (Poaceae, tribe Poeae) and implications for taxonomy of the genus
Bibliographic record
Abstract
Taxonomic uncertainty exists regarding the circumscription of the following seven phenotypically similar Australian species: Poa crassicaudex Vickery, P. hookeri Vickery, P. labillardierei Steud., P. phillipsiana Vickery, P. poiformis (Labill.) Druce, P. porphyroclados Nees (including P. serpentum Nees) and P. sieberiana Spreng. Multivariate ordination and clustering analyses of morphological data were conducted and the distribution of morphological diversity among taxa was assessed for congruence with current taxonomic boundaries. One-way analyses of variance and Tukey’s honest significant difference tests were applied to identify continuous characters that differentiate taxa. Utility of morphological characters was assessed in light of the distribution of variation among and within taxa. Revisions of P. labillardierei, P. porphyroclados and P. sieberiana circumscriptions are proposed. Accounting for nomenclatural priority, proposed revisions include recognition of P. porphyroclados vars. acris, labillardierei, porphyroclados, and. serpentum, P. sieberiana var. cyanophylla Vickery at species rank, and P. phillipsiana at varietal rank within P. sieberiana. Species boundaries are supported by leaf, culm, panicle, spikelet and floret dimensions. The present study enables increased accuracy in taxonomic identifications for Poa species that are keystones in a range of grassland vegetation types, including critically endangered natural temperate grassland and eucalypt woody grassland ecosystems, therefore contributing to the effective biodiversity monitoring and management of these ecosystems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".