Long-range forecasts: Linseed oil and the hemispheric movement of market and climate data, 1890–1939
Bibliographic record
Abstract
Crop and weather forecasting are some of the least predictable elements of agri-business, and public and private sector interests have developed different approaches to improving results in each area. This article examines how organisations produced, acquired, and shared the environmental knowledge they needed for success in the increasingly global supply chains of agri-business. Crop knowledge was extensive and growing in the late nineteenth century, including a series of nascent forecasting methods. Climate knowledge was limited and retreating because of underfunding and spurious theories about solar radiation. But the records of Archer-Daniels-Midland (ADM) and crop scientists in the Northern Great Plains show that linseed oil manufacturers created extensive knowledge networks to gather crop and some climate information in almost real time. Business associations served an asymmetrical role in these knowledge networks, and some manufacturers, like the members of the Flax Development Committee, treated scientists as a crop reporting service. As Argentina became a major linseed producer the US oilseed sector used public and private intermediaries to develop specialized knowledge of grassland agriculture in both the Prairies and the Pampas.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".