Validating the BERMS in situ Soil Water Content Data Record with a Large Scale Temporary Network
Bibliographic record
Abstract
Calibration and validation of soil moisture satellite products requires data records of large spatial and temporal extent and for diverse land cover types. Obtaining these data, especially for forests, can be challenging. These challenges can include the remoteness of the locations and expense of equipment. The Boreal Ecosystem Research and Monitoring Sites (BERMS) network in Saskatchewan, Canada, is a long‐term ecosystem network which includes five soil water content profile stations. These stations provide a critical but incomplete view of the soil water content patterns across a study domain of 10,000 square km; however, the representativeness of these observations for this purpose has not yet been evaluated. In coordination with the Canadian Experiment‐Soil Moisture 2010 (CANEX‐SM10), a temporary network of surface soil water content sensors was installed during the summer of 2010 to enhance the data resources of the BERMS network. This short term data record was then used as a basis for up‐scaling and validating the BERMS network. This large domain is approximately 1200 square km and provides a higher confidence because of the increased number of sampling sites. Using temporal stability analysis, this network verified that the BERMS network could be scaled to a satellite scale footprint with a root mean square error (RMSE) of 0.025 m 3 m −3 , and applied to the entire period of record.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".