Physically-based canopy reflectance model inversion of forest structure from MODIS imagery in boreal and mountainous terrain using the BIOPHYS-MFM algorithm
Bibliographic record
Abstract
The Multiple Forward Mode Biophysical Structural (BIOPHYS-MFM) canopy reflectance model inversion algorithm was used to derive forest structural outputs from Terra-MODIS imagery over boreal forest and mountainous terrain. Validation results expressed as average differences against field data showed boreal and mountain retrieval accuracies for forest density within 350 and 800 stems/ha, respectively, with canopy radius on the order of 1m or less for most plots. Accuracies were generally higher in boreal forest than mountainous terrain, with the latter improved using advanced topographic correction. Higher stem density accuracies were obtained for pine versus spruce in both settings. These levels of results from MODIS are appropriate for follow-on input to carbon and other ecosystem models as well as regional-scale forest inventories and other programs.
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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 | 0.000 | 0.001 |
| 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 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".