Allometric models for predicting the aboveground biomass of Canada yew (Taxus canadensis Marsh.) from visual and digital cover estimates
Bibliographic record
Abstract
The objectives of this study were to evaluate visual and digital estimates of percent cover as source data and to develop cover-based allometric models for the prediction of aboveground biomass of Canada yew ( Taxus canadensis Marsh.). Cover was determined from visual assessment and digital images captured over 25 plots (1 m2) at a model training site near Timmins, Ontario. Linear and power functions were fit to the cover–biomass data to develop models of foliage, stem, and total aboveground biomass. Both sources of cover data produced models that explained between 70% and 85% of the variance in the training data, with root mean square error estimates ranging from 27 g·m–2 (foliage) to 85 g·m–2 (total). Models based on visual cover data performed consistently better and were tested on independent data. Stem and total biomass were underestimated in the model testing data set; however, prediction statistics indicated that the linear and power forms of foliage biomass models were validated by the testing data. Final models of foliage biomass were developed from the entire data set, with mean absolute errors of 18.3 and 18.7 g·m–2 for the linear and power forms, respectively. Additional variables (e.g., plant height, age) may be required to provide general predictions of the woody biomass of Canada yew.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".