Advanced Efficient Feature Selection Integrating Augmented Extreme Learning Machine and Particle Swarm Optimization for Predicting Nitrogen Use Efficiency and Yield in Corn
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Efficient nitrogen management is crucial for improving corn productivity while minimizing environmental impacts. This study evaluates the response of corn to nitrogen fertilization using three key metrics: yield; nitrogen harvest index (NHI); and agronomic nitrogen use efficiency (ANUE). This experiment was conducted over three years (2021–2023) across 84 sites in Quebec, Canada, with five nitrogen treatments applied post-emergence (0, 50, 100, 150, 200 kg N/ha) and initial nitrogen applied at seeding (30 to 60 kg/ha). In addition, various soil health indicators, including physical, chemical, and biochemical properties, were monitored to understand their interaction with nitrogen use efficiency. Machine learning techniques, such as augmented extreme learning machine (AELM) and particle swarm optimization (PSO), were employed to optimize nitrogen recommendations by identifying the most relevant features for predicting yield and nitrogen use efficiency (NUE). The results highlight that integrating soil health indicators such as enzyme activities (β-glucosidase [BG] and N-acetyl-β-D-glucosaminidase [NAG]) and soil proteins into nitrogen management models improves prediction accuracy, leading to enhanced productivity and environmental sustainability. These findings suggest that advanced data-driven approaches can significantly contribute to more precise and sustainable nitrogen fertilization strategies.
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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 it