Linear mixed-effects models for estimating biomass and fuel loads in shrublands
Bibliographic record
Abstract
Shrubland biomass is important for fire management programmes and for carbon estimates. Aboveground biomass and the combustible portion of biomass, the fuel load, in the past have been measured using destructive techniques. These techniques are detailed, highly labour intensive, and costly; hence, an alternative approach was sought. The new approach used linear mixed-effects models to estimate biomass and fuel loads from easily measured field variables: shrub overstorey height and cover, and understorey height and cover. Site was regarded as a random effect. Sampling sites were located throughout New Zealand and included a range of shrubland vegetation types: manuka ( Leptospermum scoparium J.R. Forst. et G. Forst.) and kanuka ( Kunzea ericoides (A. Rich.) J. Thomps.) scrub and heath, pakihi (mixed low heath, fern, and rushes), and gorse ( Ulex europaeus L.). The approach was extended and confidence intervals were constructed for the regression models. Statistical analysis showed that understorey height and overstorey cover were significant (at the 5% level) in some cases. Overstorey height was highly significant in all cases (p < 0.0001), allowing development of models useful to the operational user. The models allow rapid estimation of average fuel loads or biomass on new sites, and double sampling theory can be applied to calculate the error in the resultant biomass estimate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".