Exploring trans-regional harvesting operation patterns based on multi-scale spatiotemporal partition using GNSS trajectory data
Bibliographic record
Abstract
Identifying agricultural machinery operations is crucial for enhancing agricultural productivity and promoting the transition to data-driven agriculture. Current research focuses solely on administrative divisions, overlooking the links between machinery movement, natural spatial patterns, and spatiotemporal dependencies. The direct clustering of GNSS points is inefficient and incurs substantial computational costs. In response to these challenges, we introduce an unsupervised clustering method based on multiscale spatiotemporal partitioning, which systematically integrates spatial and temporal dimensions to analyze GNSS trajectory data. By designing multiscale grids and temporal partitions, we efficiently processed high-dimensional trajectory data by employing t-SNE and K-means++ algorithms for dimensionality reduction and clustering, and the visualization validated the clustering effectiveness. When applied to GNSS data from the wheat harvest season in China, the results revealed distinct patterns of harvester movement, including trans-regional movement trends. The geogrids are clustered into four groups, each of which exhibits a distinct spatiotemporal relationship. A combined geogrid analysis with administrative regions identified Anhui as having the highest flow density, whereas Henan had the most concentrated areas of trans-regional harvester flow. These findings offer valuable insights for planning harvester operations, particularly in trans-regional harvester management, by understanding complex spatiotemporal dynamics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".