Investigation of linear spectral mixtures of the reflectance spectra using laboratory simulated forest scenes
Bibliographic record
Abstract
In this study, we used laboratory data to investigate the effects of multiple scattering between tree crowns and snow background on the linearity of mixtures of the reflectance of winter forest scenes. Several scenes were designed in the laboratory to simulate the natural forest winter landscape. Hyperspectral images of the designed scenes were acquired by the Compact Airborne Spectrographic Imager (CASI). Each scene was decomposed by linear spectral unmixing of the scene reflectance spectrum using sunlit crown, shaded crown, sunlit background, and shaded background as end members. The SPRINT canopy model was employed to evaluate the results of the linear spectra unmixing approach. Our results show that if a linear unmixing approach is used for the designed scenes, the errors in the fractions of end members are as high as 25 percent relative to the fractions obtained by the SPRINT model. This investigation suggests that non-linear mixture models may be needed to account for the multiple scattering between tree crowns and snow background.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".