Forum: Critical Decision Dates for Drought Management in Central and Northern Great Plains Rangelands
Bibliographic record
Abstract
Ranchers and other land managers of central and northern Great Plains rangelands face recurrent droughts that negatively influence economic returns and environmental resources for ranching enterprises. Accurately estimating annual forage production and initiating drought decision-making actions proactively early in the growing season are both critical to minimize financial losses and degradation to rangeland soil and plant resources. Long-term forage production data sets from Alberta, Kansas, Montana, Nebraska, North Dakota, South Dakota, and Wyoming demonstrated that precipitation in April, May, and June (or some combination of these months) robustly predict annual forage production. Growth curves from clipping experiments and ecological site descriptions (ESDs) indicate that maximum monthly forage growth rates occur 1 mo after the best spring month (April to June) precipitation prediction variable. Key for rangeland managers is that the probability of receiving sufficient precipitation after 1 July to compensate for earlier spring precipitation deficits is extremely low. The complexity of human dimensions of drought decision-making necessitates that forage prediction tools account for uncertainty in matching animal demand to forage availability, and that continued advancements in remote sensing applications address both spatial and temporal relationships in forage production to inform critical decision dates for drought management in these rangeland ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".