Defining Shaded Spectra by Model Inversion for Spectral Unmixing of Hyperspectral Datasets - Theory and Preliminary Application
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The potential of using hyperspectral imagery of canopies to retrieve vegetation and soil information using spectral mixture analysis (SMA) techniques has been the focus of several recent studies. The SMA method estimates the proportion of pixel area that can be attributed to a cover type with a unique spectral profile. Shaded leaf, shaded residue, and shaded soil areas are generally ignored, or treated as equivalent. <p> This paper presents a method of determining shaded spectral reflectance profiles for component cover types by determining the mean multi-scattering ratio (the ratio of shaded-to-sunlit reflectance) and applying that mean to measured sunlit component spectral reflectance. In this method, the multi-scattering ratio is determined by FLAIR model inversion. The resulting component shaded spectral reflectance can then be used as part of the SMA.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it