Twentieth Century Crop Statistics, 1900-2017
Bibliographic record
Abstract
From original website The Twentieth Century Crop Statistics, 1900-2017 data set consists of national or subnational maize and wheat production, yield, and harvested area statistics for all available years for the period 1900-2017. It combines a new digitization of crop statistics from Italy, Spain, Indonesia, China, Mexico, Uruguay, Chile, Sweden, and Morocco with existing, publicly available, digitized data sets from India, Australia, the United States, Canada, Southern Brazil, Argentina, England, Austria, Belgium, Croatia, Czech Republic, Finland, Germany, Spain, Portugal, France, the Netherlands, and South Africa. All Units are converted to hectares (ha) for Units of harvested areas, tonnes for Units of production, and tonnes/ha for Units of yield. A ratio of 1/36.744 is used to convert wheat bushels to tonnes, and a value of 1/39.368 is used to convert maize bushels to tonnes. In all cases, the Harvest_year reported in the data set is the harvest year for the crop. Comment Metadata are included in various formats. Details about the methodology are in the documentation (pdf).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.078 |
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".