HarvestStat: a global effort towards open and standardized sub-national agricultural data
Bibliographic record
Abstract
Abstract Agricultural production statistics underpin diverse research efforts and development activities. Yet despite their critical importance, efforts to collate, update, and harmonize detailed sub-national agricultural production statistics are frequently redundant and incomplete due to the substantial time, effort, and resources required. The persisting lack of coordination and standards in the food systems data community wastes valuable resources and hinders advances in action-oriented food systems knowledge. Here we introduce the HarvestStat sub-national data consortium as an open-source, collaborative, and transparent model to overcome these challenges. HarvestStat is collaboratively producing publicly available databases and datasets for the food systems community and the broader environmental and sustainability sciences by moving beyond closed and disjointed data-gathering efforts. We are guided by core principles of complete data openness—prioritizing high standards of quality assurance; active inclusion—emphasizing involvement from local experts; and collaboration—fostering engagement across communities of data producers and users. We extend an open global call to action, inviting organizations and individuals to engage in advancing this critical agenda.
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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 | 0.160 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.027 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.008 | 0.025 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".