Great Plains Precipitation Gradient Changes with Latitude
Bibliographic record
Abstract
Change in annual precipitation across the U.S. Great Plains. Annual precipitation varies greatly from east to west across the semi-arid U.S. Great Plains where precipitation is the primary factor affecting yield. Dryland farmers would have a better means of understanding the applicability of cropping systems research done in one part of the Great Plains to their specific location if they were aware of the rate of change of precipitation with east–west direction at their latitude. In an article recently published in Agricultural & Environmental Letters, David Nielsen presents the quadratic relationship between latitude and the east–west rate of change in precipitation for the U.S. Great Plains. That rate of change is nearly constant (8.5 km for a 10 mm change or 13 miles for an inch change in annual precipitation) between 31oN and 38oN. Further north, however, the east–west gradient increases curvilinearly, reaching a maximum of 42 km for a 10 mm change or 66 miles for an inch change at 49oN on the U.S.−Canada border. The quadratic relationship serves as a decision support aid to help farmers determine the applicability of research results that may have been acquired many miles east or west of their locations with significantly different annual precipitation. Adapted from Nielsen, D.C. 2018. Influence of latitude on the US Great Plains east–west precipitation gradient. Agric. Environ. Lett. 3:170040. View the open access article online at https://doi.org/10.2134/ael2017.11.0040
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".