Reducing the Dimensionality of Plant Spectral Databases
Bibliographic record
Abstract
Ground-based measurements of plant reflectance and transmittance are essential for remote sensing projects oriented toward agriculture, forestry, and ecology. This paper examines the application of principal components analysis (PCA) in the storage and reconstruction of such plant spectral data. A novel piecewise PCA approach (PPCA), which takes into account the biological factors that affect the interaction of solar radiation with plants, is also proposed. These techniques are compared through experiments involving the reconstruction of reflectance and transmittance curves for herbaceous and woody specimens. The spectral data used in these experiments were obtained from the Leaf Optical Properties Experiment (LOPEX) database. The reconstructions were performed aiming at a root-mean-square error lower than 1%. The results of these experiments indicate that PCA can effectively reduce the dimensionality of plant spectral databases from the visible to the infrared regions of the light spectrum, and that the PPCA approach can further maximize the accuracy/cost ratio of the storage and reconstruction of plant spectral reflectance and transmittance data.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".