Use of growth characteristics for predicting plant age of three obligate-seeder Proteaceae species
Bibliographic record
Abstract
We tested the ability to predict plant (and hence population) age for three fire-sensitive obligate-seeder Proteaceae species (Banksia ericifolia, Banksia marginata and Petrophile pulchella) in the heath and woodland vegetation of the Sydney region. To do this we sampled the number of growth whorls, as well as other growth characteristics (stem girth and height measurements, and canopy area and volume estimates), in areas of known time since last fire (TSLF). The average number of growth whorls was a very good predictor of plant age for both Banksia species (R2 = 98%, 99%), but this needed to be corrected for linear underestimation in P. pulchella (R2 = 92%). This technique could successfully be applied to these species in similar habitats across a large spatial scale, and so this information can be used to determine the age of a population in areas of unknown TSLF. A sample size of 15 plants was sufficient for accurate age estimates of all species; however, better estimates of TSLF for a particular plant community were obtained when estimates from two or more of the species were combined. We thus provide empirical evidence for the validity and accuracy of the growth-whorl technique for predicting plant age and hence TSLF. This information will assist in informing the development of appropriate management strategies for plants in relation to fire. Of the other growth characteristics studied, stem girth was the most reliable predictor; however, in general these other characteristics had wide confidence intervals on the predictions for sites greater than 10 years TSLF, owing to a non-linear relationship with age.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".