Collection: Datasets From Real-Time In-Situ Soil Monitoring for Agriculture 2025
Bibliographic record
Abstract
TheReal-Time In-Situ Soil Monitoring for Agriculture(RISMA) dataset is a collection of publicly available high-quality soil volumetric water content (VWC), soil temperature, and meteorological data for agricultural regions in Manitoba, Saskatchewan, and Ontario, Canada. The RISMA network was established beginning in 2011, and data collection continues at the time of publication. Currently, datasets are available for 36 VWC monitoring stations, where sensors are located within annually cropped and pasture sites. Available data varies depending on location but include soil VWC and soil temperature from surface to as deep as 1.5 m, rainfall, air temperature, relative humidity, wind speed, wind direction, and solar radiation. The RISMA stations cover a wide variety of soil types, from clay and clay loams to sandy loams and sand. The data are processed using an automated script which includes a quality control process. This dataset is valuable for researchers working in agriculture, soil science, meteorology, and remote sensing. Data are used to calibrate and validate remote sensing products as well as hydrological, meteorological, and agricultural models. Sites within Manitoba were extensively detailed as core validation sites for NASA’s Soil Moisture Active Passive (SMAP) satellite.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.025 |
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".