Use of the National Drought Model (NDM) in Monitoring Selected Agroclimatic Risks Across the Agricultural Landscape of Canada
Bibliographic record
Abstract
The National Drought Model (NDM) is an amalgamation of the atmospheric component of the original Palmer Drought and Versatile Soil Moisture Budget (VSMB) models. The NDM uses locally derived coefficients from the station or gridded climate data to calculate a calibration factor for comparing locations in time and space. A modular approach is used to model major processes such as evapotranspiration, biometeorological time, snowmelt, and the cascading of soil moisture down to the root zone. The modular approach allows modifications to be made to specific sections without making structural changes to the entire model or the data inputs. The NDM is an operational tool, integrating data from the climate, soil, and plant sciences to monitor agroclimatic risks such as drought and excess moisture. In this paper, the capacity of the NDM to monitor extreme agroclimatic risks, such as drought and flooding of agricultural soils, was assessed. Using the Palmer Drought Severity Index component of the NDM, the mapping of the spatial extent and severity of the 2001 and 2002 droughts across Canada and the excess moisture conditions on the Canadian Prairies in 2010 agreed with other assessments. The validation study of soil moisture at two Alberta locations (Lethbridge and Beaverlodge) showed that the VSMB tracked the soil moisture flux in the root zone successfully in response to changing environmental conditions. The VSMB explained about 70 and 60% of the variance in observed soil moisture at the two respective locations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".