Estimating Fresh Weight of Individual Pea Shoots Using Measurable Morphological Characteristics
Bibliographic record
Abstract
Nondestructive estimation of individual shoot fresh weight (FW) from its measurable morphological traits is useful for a wide variety of purposes in pea shoot production. To predict individual shoot FW, nine regression models in total were developed, including two power models using stem diameter (SMD) or stem length (SML) as a variable, and seven linear models using part or all the following variables: SMD, SML, leaflet length (LL), leaflet width (LW), stipule length (SEL), and stipule width (SEW). Among the nine models, the 6-variable linear equation had the highest coefficient of determination, R 2 = 0.92, indicating it is most effective at explaining the variation in FW. The linear equations including only one variable, SMD or SML, were equally the least effective as nonlinear equations (i.e., power models). This finding suggests that there was a linear rather than nonlinear relationship between FW and the morphological variables. During stepwise regression, SEW and LW together were first removed from the 6-variable linear models without reducing the R 2 , and then SEL, SMD, SML were further removed one-by-one, which reduced the R 2 from 0.92 to 0.90, 0.85, and 0.71, respectively. The result suggests that SMD, SML, SEL, and LL were the most important four predictor variables for multivariable linear regression models to estimate FW, an idea that was also supported by path analysis. For the four linear models with 1–4 predictor variables from stepwise regression, the prediction accuracy of FW was evaluated based on the agreement between the predicted and measured values using another independent dataset. The 4- and 3-variable linear models (i.e., FW = −1.437 + 0.276 SMD + 0.010 SML + 0.022 LL + 0.013 SEL and FW = −1.383 + 0.308 SMD + 0.011 SML + 0.030 LL, respectively) were selected for their more accurate prediction than 1- and 2-variable linear models and relatively simpler forms than a 6-variable linear model. Although the prediction accuracy can be potentially affected by air temperature, light conditions, and harvesting time, the multilinear regression model is an effective approach for estimating fresh weight of individual pea shoots using its measurable morphological traits.
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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".