Econometric Implications of Rectangular Hyperbolic Crop–Weed Competition
Bibliographic record
Abstract
Estimation of the rectangular hyperbolic model of crop–weed competition by the method of maximum likelihood is investigated. Econometric implications of the rectangular hyperbolic form and normal stochastic specification are derived from the associated Fisher information matrix. Analysis explains some of the experience with the model in empirical work and clarifies the nature of statistical data that are advantageous to parameter estimation. Numerical examples are used to illustrate the relationship between the econometric problem and underlying information. Results provide agricultural economists with sound reasons for recommending specific changes in data collection choices although, in contrast to some existing perceptions, only some fairly minor changes in commonly used experimental design will be necessary to meet agricultural economists' research objectives. L'évaluation du modèle hyperbolique rectangulaire de la concurrence entre la récolte et les mauvaises herbes par la méthode de maximum de vraisemblance est étudiée. Des implications économétriques de la forme hyperbolique rectangulaire et du cahier des charges stochastique normal sont dérivées de la matrice associée de l'information de Fisher. L'analyse explique une partie de l'expérience avec le modèle dans le travail empirique et clarifie la nature des données statistiques qui sont avantageuses à l'évaluation de paramètre. Des exemples numériques sont employés pour illustrer le rapport entre le problème économétrique et l'information fondamentale. Les résultats fournissent aux économistes agronomes des raisons saines pour recommander les changements spécifiques des choix de collecte de données bien que, contrairement à quelques perceptions existantes, seulement quelques changements assez mineurs de conception expérimentale généralement utilisée seront nécessaires pour répondre aux objectifs des recherches des économistes agronomes.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".