Machine Learning-Assisted In Vitro Rooting Optimization in Passiflora caerulea
Bibliographic record
Abstract
In vitro rooting as one of the most critical steps of micropropagation is affected by various extrinsic (e.g., medium composition, auxins) and intrinsic factors (e.g., species, explant). In Passiflora species, in vitro adventitious rooting is a difficult, complex, and non-linear process. Since in vitro rooting is a multivariable complex biological process, efficient and reliable computational approaches such as machine learning (ML) are required to model, predict, and optimize this non-linear biological process. Therefore, in the current study, a hybrid of generalized regression neural network (GRNN) and genetic algorithm (GA) was employed to predict in vitro rooting responses (rooting percentage, number of roots, and root length) of Passiflora caerulea based on the optimization of the level of auxins (indole-3-acetic acid (IAA), indolebutyric acid (IBA), and 1-naphthaleneacetic acid (NAA)) and the type of explant (microshoots derived from leaf, node, and internode). Based on the results, the GRNN model was accurate in predicting all in vitro rooting responses of P. caerulea (R2 > 0.92) in either training or testing sets. The result of the validation experiment also showed that there was a negligible difference between the predicted-optimized values and the validated results demonstrating the reliability of the developed GRNN-GA model. Generally, the results of the current study showed that GRNN-GA is a reliable and accurate model to predict and optimize in vitro rooting of P. caerulea.
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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 | 0.000 | 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 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".