Comparison of Different Methods for Assessing Chlorophyll Content in Citrus
Bibliographic record
Abstract
The content of chlorophyll-Chl present in green vegetables is strongly associated with the momentary state of the plant photosynthesis. Conventionally, determination of chlorophyll is by extraction with organic solvents and determining a spectrophotometer, and this method expensive, laborious and moreover a destructive method. Thus, the use of portable equipment has been used instead, as they allow studies without destruction of leaf tissue and obtaining instantaneous measurements. Thus, the aim of correlating the chlorophyll results obtained in the laboratory with Falker Chlorophyll Index (FCI) obtained by the ClorofiLog in different citrus rootstocks, as well asits relationship to soil moisture content variation. Positive simple correlations (linear Pearson correlation) were obtained for chl a, b and total, with the lowest correlation values observed for chl β, which ranged from 0.470-788 among the analyzed rootstocks. The coefficients of determination for the three variables chl α, β and total α+β showed a better fit by the polynomial regression model in all analyzed rootstocks; in general, the best results were for chl α, ranging from 0.7093-0.8551. The results indicate the usefulness of the ChloroflLog apparatus in the indirect determination of chlorophyll content in citrus.
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.
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 | 0.001 | 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 | 0.001 | 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".