Accuracy and bias on the estimation of aboveground biomass in the woody vegetation of the Sonoran Desert
Bibliographic record
Abstract
We developed allometric regressions for predicting aboveground biomass (AGB) in the Sonoran Desert. Information on canopy cover and height was collected and used to predict AGB from plant dimensions in twenty 25 m 2 plots that were also fully harvested. The comparison of these two methods showed that allometric equations without correction for bias led to gross AGB underestimation (four times lower than the true values for uncorrected logarithmic allometric equations). Among the tested correction factors, the ratio estimator highly reduced bias and increased accuracy. Validation of allometric estimates with whole-plot harvesting defined the best equation and the least biased correction factor. However, simple nonlinear power functions also gave accurate and unbiased estimates of AGB. We recommend the use of nonlinear models in lieu of traditional logarithm-transformed models. Correction for bias and field verification should be considered in allometric regressions used to predict AGB. In the absence of validation by direct biomass measurements, allometric predictions derived from linearization of ln-transformed data should be taken with care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".