Spectroscopic determination of leaf water content using continuous wavelet analysis
Bibliographic record
Abstract
Leaf water content as a percentage of dry mass (LWCD), also termed fuel moisture content, is a critical variable for fire risk modeling. To date, the relationship between spectral reflectance and LWCDis poorly understood due to the mixed effect of leaf water and dry mass on spectral response. Few studies have reported an adequate estimation of LWCDfrom leaf reflectance spectra, in particular for a variety of species. In this study, we propose an effective approach, using continuous wavelet analysis, to determine LWCDfrom reflectance spectra for 47 tropical forest species in Panama. The proposed method provides a new way to select a small number of wavelet features (wavelet coefficients) that are sensitive to changes in LWCD. Wavelet features derived using this new method are compared with common spectral indices in terms of their relationship with LWCD. Results demonstrate that LWCDis poorly correlated to the water indices but strongly correlated to the derived wavelet features. Accurate estimates of LWCDacross a range of species are achieved using any of those wavelet features and can be slightly improved using a combination of them.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".