Soil Moisture Active/Passive Validation Experiment Within the Canadian Boreal Forest in 2022 (SMAPVEX22-Boreal)
Bibliographic record
Abstract
The soil moisture active passive (SMAP) validation experiment in the boreal forest took place near Candle Lake, Saskatchewan within an area previously studied as a part of the experiments conducted at the Boreal Ecosystem Research and Monitoring Sites (BERMS). This study specifically focuses on the data collected within a single SMAP radiometer footprint, covering an area of approximately 30 × 30 km2 within a boreal forest environment. The land cover classes and soil types in the boreal forest area of the BERMS region are dominated by coniferous trees and soils represented by an often-thick, organic-rich layer of moss and leaf litter above a mineral soil layer. The completed field experiment focused on accurately representing both soils and vegetation of the region based on comprehensive sampling campaigns completed over two sampling periods between 16–30 June and 6–18 August 2022. Numerous datasets were collected from sampling campaigns and through the installation of in situ networks. Comparisons among observed soil moisture (SM) and those retrieved from the SMAP and soil moisture and ocean salinity mission satellite platforms show a significant correlation to SM collected in situ. It is anticipated that the data collected as a part of this experiment (vegetation biomass and soil and vegetation structures) will be important for modeling efforts to improve SM retrievals in these environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".