Spectral Characteristics of Natural and Laboratory-Induced Leaf Senescence in Four Common North American Tree Species
Bibliographic record
Abstract
Two laboratory techniques (air drying and oven drying) were implemented to compare the spectral reflectance differences between laboratory-induced and natural leaf senescence. The spectral signatures of four common North American tree species were recorded over the 2011 summ erautumn season. Natural ‘on-tree’ senescence was measured throughout the senescence period and simultaneously with the two laboratory methods. Two substantial differences were observed between the natural senescence and laboratory techniques: the persistence of the ‘green peak’ for the laboratory methods; and the much higher reflectance values throughout the middle infrared region for the laboratory methods. These results suggested that a more sophisticated laboratory method including the ability to export leaf constituents during senescence is required to accurately reproduce autumn leaf senescence in a laboratory setting. If achieved, this could greatly improve analyst’s ability to reproduce the spectral changes associated with vegetation health and leaf senescence.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".