An approach for the analysis of vegetation spectra using non-linear mixed modeling of truncated power spectra
Bibliographic record
Abstract
Analysis of vegetation spectra is often characterized by an adverse ratio of sample size to number of wavelengths.A reduction in the dimensionality of the spectra is needed to ensure consistent estimates.We propose a reduction based on a non-linear mixed modeling of power spectra transforms of truncated Fourier series representations of vegetation spectra.Two sets of foliage spectral data obtained from balsam fir (Abies balsamea) exposed to different silvicultural regimes and three eucalypt species (Eucalyptus spp.) demonstrate the method.Only the first 42 frequencies in a power spectrum contributed significantly to the variance of a spectrum.Power spectra were dominated by a small number of low frequencies; the influence of frequency was described well by an exponentiated quadratic polynomial model with significant fixed and random effects.Model parameters can be subject to physiological inference and hypothesis testing. nonlinear-mixed model / Fourier transform / power spectra / hypothesis testing / classificationRésumé -Méthode d'analyse des spectres de végétation par modélisation mixte non linéaire des spectres de puissance tronqués.L'analyse des spectres de végétation est souvent caractérisée par un rapport négatif entre la taille de l'échantillon et le nombre de longueurs d'ondes.Une réduction de la dimension des spectres est nécessaire pour garantir des estimations uniformes.Nous proposons une réduction fondée sur une modélisation mixte non linéaire des transformées de puissance spectrale des représentations de séries de Fourier tronquées visant des spectres de végétation.Pour ce faire, nous utilisons deux ensembles de données spectrales du feuillage de sapins baumiers (Abies balsamea) exposés à différents traitements sylvicoles et de trois espèces d'eucalyptus (Eucalyptus spp.).Seules les 42 premières fréquences de puissance spectrale ont contribué de façon appréciable à sa variance.Un petit nombre de basses fréquences dominaient les puissances spectriques ; l'effet de la fréquence a été bien décrit à l'aide d'un modèle polynomial quadratique d'exponentiation comportant des effets fixes et aléatoires appréciables.Les paramètres du modèle peuvent faire l'objet d'analyse de l'hypothèse et d'une inférence physiologique. modèle mixte non-linéaire / transformation Fourier / répartition spectrale / tests des hypothèses / classification
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".