Evaluation of Time After Leaf Collection As An Influential Factor In Spectroradiometric Measurements
Bibliographic record
Abstract
Background: Spectroradiometric studies involving the collection of vegetative material in the field and subsequent radiometric measurements in the laboratory are susceptible to limitations due to the loss of information related to the temporal lapse between collection and analysis.Objective: Thus, the objective of this study is to identify which spectral intervals and which vegetation indices present higher sensitivity to the postharvest time of leaves extracted from an individual of the species Eucalyptus saligna Smith.Material and Methods: The material was collected in the field, and the radiometric measurements were performed in laboratory using a FieldSpec®3 spectroradiometer, with an interval of 15 minutes, during 10 hours of observation.Epectral bands and indexes with frequent use in vegetation studies that evolve both in situ spectroradiometry techniques and orbital sensor surveys were selected.For each of the variables, were computed the averages of the 40 observations over the time and Tukey's test of means comparison was applied pair by pair.Results:In general, bands and indices that respond directly by vegetative vigor and photosynthetic activity showed higher latency on significance in relation to the beginning of the observations, as showed by SR, NDVI and REP, with 5, 5, 25, 9.75 hours respectively.However, the variables related to the internal scattering due to the tissue rupture and water loss were more sensitive to the experiment, especially the TVI index and the NIR band, which demonstrated significant results after 1.25 and 1.5 hours of the beginning of the measurements.Conclusion:The listed variables allowed the detection of foliar degradation over time after collection at different levels of sensitivity.For the latter it is recommended that in similar experiments it is timely to observe the waiting time between the extraction of leaves and the performance of the radiometric measurements in order to avoid possible loss of information.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.000 |
| 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".