Exploring the mechanism of grey forecasting models: A perspective from dynamic system modelling
Bibliographic record
Abstract
Abstract The grey forecasting model has been developed for forty years, but there are various understandings of its modelling ideas. The research motivation of this paper is to provide an insight into the understanding of grey forecasting models and demonstrate how grey forecasting model can solve practical problems. Based on the modelling process of grey forecasting models, this paper first discusses the model mechanism from the perspective of dynamic system modelling and describes the model application using perishable products inventory as an example. Then, we introduced the modelling processes and characteristics of grey forecasting models under traditional and direct methods. The research issue of grey forecasting models is summarized by analysing model characteristics, the complete process of model establishment is presented, and the mechanism of each modelling process is elaborated in detail. Next, taking the inventory of perishable products as an example, we discuss how the grey forecasting model solves practical problems, and illustrate the application process of the grey forecasting model through a numerical example of citrus.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".