Utilization of neural networks for the estimation of aboveground forest biomass from Ikonos satellite image and multi-source geo-scientific data
Bibliographic record
Abstract
The present study develops an approach for the estimation of aboveground forest biomass based on neural networks, using Ikonos satellite image data and multi-source geo-scientific data. Two methods of aboveground forest biomass estimation were compared: multiple regressions and the neural networks. Percentages of residual errors of the neural networks biomass estimates were lower than 1% for all groups of species, except for "intolerant hardwood" which had a percentage of 3.2% for the 9-18 cm DBH class. Percentages of residual errors of biomass estimates were higher with the quadratic multiple regression approach than with the neural networks, particularly for "intolerant hardwood" where a value of 51.41% was observed for the 19-40+ cm DBH class. Root mean square error values (RMSE) calculated from biomass estimates resulting from the neural networks approach were lower than those computed with estimates of the quadratic multiple regressions model, for all groups of species.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| 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".