Inter- and intra-specific variation in phyllode size and growth form among closely related Mimosaceae Acacia species across a semiarid landscape gradient
Bibliographic record
Abstract
The mulga complex (Acacia aneura F. Muell ex Benth and closely related species) consists of woody trees and shrubs, and is distributed across 20% of the Australian continent. A. aneura is renowned for a wide variety of phyllode shapes and growth forms, which may co-occur at any one site. We examined the intra- and inter-specific variation in growth form and phyllode shape in four species of the mulga complex, including A. aneura, across topographic gradients in semiarid north-west Australia. We measured 792 trees across 28 sites stratified into six discrete landscape positions; upper slope, lower slope, low open woodland, banded woodland, low woodland, and drainage line. Dominance of phyllode shapes was strongly related to landscape position. A. aneura with terete phyllodes were dominant on the hill slopes, whereas broad phyllodes were most common on A. aneura in all valley woodlands. Trends in growth form were less distinct, although single-stemmed forms were more common on hills, whereas the valleys had more multi-stemmed forms. The quantification of growth form and phyllode shape variability within the mulga complex provides a basis for the quantitative determination of functional links between morphology and environmental conditions at both the site and landscape level.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".