Time Series Similarity Analysis Framework in Fresh Produce Yield Forecast Domain
Bibliographic record
Abstract
Searching similarity in time series (TS) datasets has gained widespread attention lately in databases classification and forecast domain. In this study, a TS similarity detection framework is proposed to explore alike-behavior fresh produce (FP) in the yield forecast domain through several factors. The sequential daily yield datasets of three types of FP, including strawberry, raspberry, and blueberry, as well as environmental information related to the Santa Maria region, California, between the years 2011 to 2019, are used to develop and evaluate the models. The framework's output is decided to be the similarity percentage (SP) by considering some thresholds that have been tuned using several synthetic yield datasets. According to the results, the SP is 82% and 52% for strawberry versus raspberry and strawberry versus blueberry, respectively. This indicates the fact that strawberry and raspberry have a relatively similar yield pattern compared to blueberry, which is a considerable matter in generalizing forecast models.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".