Four-scale linear model for anisotropic reflectance (FLAIR) for plant canopies. II. validation and inversion with CASI POLDER, and PARABOLA data at BOREAS
Bibliographic record
Abstract
For pt.I see ibid., vol.39, no.5, p.1072-83 (2001). To address the need for a flexible model of the bidirectional reflectance distribution function (BRDF) that is also suitable for inversion, the FLAIR Model (Four-Scale Linear Model for AnIsotropic Reflectance) has been developed H. P. White et al. (2001). Based on the more detailed Four-Scale Model J. M. Chen et al. (1997), FLAIR is a linear kernel-like model, developed with the aim of not being limited to specific canopy characteristics or view/illumination geometry, while maintaining a direct relationship between canopy architectural properties and model coefficients. Having been previously demonstrated to have the ability to capture the bi-directional patterns in both forward and inverse modes of calculation, this paper examines the FLAIR model in describing the boreal canopy by applying FLAIR to multiangular data sets obtained by various sensors during BOREAS 1994. Effects of sensor field of view, ranges of view/solar illumination geometry, and multiple sensor use on BRDF derivation and inversion for canopy parameter retrieval are considered.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".