Identifying Hydric Soils in the Lower Mainland-Fraser Valley, British Columbia: A Comparison of Methods
Bibliographic record
Abstract
In the Lower Mainland-Fraser Valley region of British Columbia, the accurate identification of hydric soils is crucial for effective wetland assessment and management amidst intense land use competition.This study compares five methods for identifying hydric soils in the region: USDA Hydric Soil Indicators (NTCHS), soil moisture regime (SMR), Actual Soil Moisture Regime (ASMR), soil drainage class (SDC), and a region-specific method, Lower Mainland-Fraser Valley (LMFV).Since there is no existing absolute standard for hydric soil, the study assessed hydric soil classifications by comparing each method's results against the NTCHS as a standard.The LMFV method exhibited the highest agreement (96%) with NTCHS, demonstrating strong correlation and minimal discrepancy, while ASMR showed the lowest agreement with NTCHS.If we regard NTCHS as the standard, the LMFV is the highest ranking in terms of accuracy, followed by RSMR, SDC, and ASMR.These findings suggest that for the Lower Mainland-Fraser Valley, methods like LMFV, which focus on detailed soil characteristics, may offer superior accuracy for hydric soil identification compared to other methods.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".