A soil moisture and precipitation dataset from the Kenaston/Brightwater Creek basin, Saskatchewan, Canada, 2007 - 2017
Bibliographic record
Abstract
The Kenaston Network, located in the Brightwater Creek basin in central Saskatchewan, is a community monitoring network with a variety of monitoring instruments. The area is a typical agricultural region with both annually cropped fields and pasture sections. Dataset presented here is from the soil moisture and precipitation sites which are spread at two spatial scales (10 km x 10 km and 40 km x 40 km) over the monitoring region. Data from the summer months (May 1 – Sept 30) is included for 2007 – 2017. Each site has at least three Stevens Hydra Probe soil moisture sensors, inserted horizontally at depths of 5, 20, and 50 cm below the surface, with sites at the 10 km x 10 km scale instrumented with an additional vertically placed sensor, measuring over the depth 0-5 cm. Parameters reported from the probes are soil temperature, real dielectric constant, and soil moisture calculated using the Stevens loam calibration equation. All sites have one of two types of tipping bucket rain gauge (Onset RG3 or Hydrological Services TB3) to provide summer precipitation totals. All data from the network have been through a quality control/quality analysis process that includes a series of automated checks followed by a manual review of all sensor parameters. **Please note: This dataset is linked to an ESSD paper at https://doi.org/10.5194/essd-11-787-2019. The authors kindly request that you reference this paper in addition to the dataset.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.013 |
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".