SPAD Index in Oregano Crop: A Proposal for Interpretation Ranges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The analysis of non-destructive variables in plants, such as the SPAD index, shows a growing trend of adoption in the field. However, it is necessary to determine comparative reference standards, aiming to assist in the interpretation of results obtained in the field and in making decisions about the management to be adopted. The study aimed to propose levels of interpretation of the SPAD index in oregano leaves based on the yield of the crop. The experiment was conducted in protected environment, randomized blocks design was adopted with four replications in 6 x 4 factorial scheme: six levels of water replacement (60, 70, 80, 90, 100, and 110% of the crop evapotranspiration-ETc) and four doses of bokashi (0, 100, 200, and 300 g m-2). For analysis, the data were subjected to variance analysis, multivariate analysis, regression and correlation. Productive management (water replacement and bokashi dose) influences the SPAD index response. Through mathematical analysis of the relationship between SPAD index and relative yield, the sufficiency ranges based on the SPAD index were determined in very low (<37) low (37-44), medium (44-46) and high (>46). The proposed classification of the sufficiency range for the SPAD index allows advances in the productive management of the oregano crop.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 it