Modeling vegetative development of berseem clover (<i>Trifolium alexandrinum</i> L.) as a function of growing degree days using linear regression and neural networks
Bibliographic record
Abstract
Accurate models of berseem clover (Trifolium alexandrinum L.) development in relation to growing degree-days (GDD) would be useful to both producers and researchers. Predictive ability of linear regression models of plant development may be limited by choice of threshold temperature and the non-linear nature of plant development. Neural networks provide a robust approach to dealing with non-linearity, and may therefore be useful for modeling plant development. In exp.1, a numerical scale of plant development was created and used to describe growth of four cultivars of berseem clover (Bigbee, Joe Burton, Saidi and Tabor) under controlled environmental conditions (constant temperature of 12, 18 or 24°C per 12-h photoperiod) for up to 18 wk of vegetative growth. Simple linear regression and neural networks were used to model plant development in relation to GDD using a range of threshold temperatures. Predictive ability of the models was compared with the results from a second controlled environment study (exp. 2). The r2 of the linear and neural models produced in exp. 1 were maximized at GDD threshold temperatures of 0 to 2°C. Results from exp. 2 indicated that the predictive ability of neural models matched or exceeded that of the linear models for all threshold temperatures evaluated. Results of the current study suggests that neural network models are relatively insensitive to base temperatures across the range tested and may therefore be preferable when a priori knowledge of temperature thresholds is not available. Key words: Berseem clover, plant development, phenology modeling, growing degree days, base temperature, neural network modeling
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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 | 0.000 |
| 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 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".