The canopy coverage is correlated with the number of shoots produced by <i>Eucalyptus</i> clones in a clonal mini-garden
Bibliographic record
Abstract
In this work, we analyzed the correlation between the canopy coverage of two commercial clones of Eucalyptus benthamii Maiden & Cambage and one of Eucalyptus dunnii Maiden and their shoot yields in a clonal mini-garden system. By canopy coverage, we referred to the area of a picture occupied by leaves (green area) when analyzed using computational resources. The mini-garden was set up to yield shoots on a regular time schedule (between 20 and 30 days) to obtain mini-cuttings for clonal propagation. Pictures were taken at approximately 30 cm above the upper leaves from the plots containing mini-stumps of each clone on the day before the collection of mini-cuttings for six consecutive harvests (approximately 6 months). The leaf coverage was obtained using the computational package Easy Leaf Area. Our results indicated a significantly high Pearson correlation coefficient (r = 0744, P < 0.001) between the canopy coverage and the number of shoots produced by each clone. A logistic regression model was adjusted to this dataset, enabling a prediction of the number of shoots based on the canopy coverage. This approach has the potential for assisting forest nurseries in predicting the yield of mini-cuttings while conducting clonal propagation of their genetic materials.
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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 | 0.001 |
| 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) | 0.001 | 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".