Importance of Sky Conditions on the Record 2004 Midwestern Crop Yields
Bibliographic record
Abstract
Weather during the 2004 growing season in the Midwest produced exceptionally high yields of all crops with resulting record yields that were 10% to 25% above prior records, an exceptional increase. Crop experts and crop-weather models failed to predict the enormous magnitude of the final yields. This inability to assess the magnitude of the 2004 crop yields resulted from a lack of information regarding the presence and effect of the numerous days with sunny skies in 2004. Clear days ranged from 43% to 110% above average across the entire Midwest, and the oddity is that these sunny days came with much-below-average temperatures and normal rainfall. Examination of climate conditions in the past 120 years reveals that when many clear skies days occurred, most summers were quite hot and dry. Only one prior summer (1927) had conditions similar to those in 2004. The summer 2004 weather conditions were unusual in other respects including the great areal extent of the favorable weather. Thus, the conditions produced record yields of all crops for the first time in history. The record yields had profound effects on crop prices, given a large foreign demand and decreasing dollar value, resulting in a huge income increase for Midwestern farmers of $14 billion. Seldom does the entire Midwest experience near uniform summer weather conditions, reflecting another unique aspect of 2004. Canadian high pressure resulting from the intrusion of 20 strong cold fronts dominated the atmospheric circulation in the central United States during the summer, limiting the movement of warm, moist air into the region and creating the high frequency of clear days. Results suggest that in the future, sky conditions measured during the growing season need to be incorporated in assessments of potential final crop yields.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".