Foliar spectral responses of sugarcane and maize: how comparable are they?
Bibliographic record
Abstract
Sugarcane and maize (corn) crops are extensively used in the production of biofuel worldwide. These plants share important physiological and morphological traits. Both belong to the group of C4 species characterized by the presence of unifacial leaves. Moreover, their stress adaptation mechanisms make them less susceptible to adverse conditions elicited by climate changes. Given these aspects and their economical value, one would expect that there is no shortage of data for these plants, particularly with respect to their foliar spectral responses. After all, such data is essential for the efficacy of precision farming and remote sensing strategies devised to obtain an ecologically sustainable increase in the yield of these crops. However, this is not the case, with the data scarcity situation being markedly more serious for sugarcane. Because of that, and considering their physiological and morphological similarities, investigations on the spectral responses of C4 plants are usually conducted using data obtained from maize specimens, with the resulting findings often being implicitly extended to sugarcane. This raises the question of whether the level of comparability between the foliar spectral responses of these two species is sufficient to support such an approach. In this paper, we aim to contribute to the elucidation of this question. Using measured reflectance data obtained for maize and sugarcane leaves, we compute selected spectral features associated with these specimens, and assess possible discrepancy trends. We then discuss data availability issues in this area, and identify relevant topics for future research that will likely require comprehensive measured spectral datasets for these plants.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".