Mapping the Unseen: Identifying Data Gaps and Proposing New Sampling Points in Northern Boreal Mountain Eco-province, BC Using K-Means Clustering and cLHS
Bibliographic record
Abstract
This study investigates the soil variability within the Northern Boreal Mountains Ecoprovince in British Columbia, with a particular focus on wetland soils and soil organic carbon mapping. Utilizing the BCSOIL2020 dataset and an array of environmental covariates, we employed Principal Component Analysis (PCA), k-means clustering, and conditioned Latin Hypercube Sampling (cLHS) to develop a comprehensive environmental covariate space. This approach allowed for the evaluation of the BCSOIL2020 dataset's representativeness of the current distribution of wetland soils and the generation of new, strategically placed sampling plots aimed at enhancing future research efforts. Through this methodology, the study identifies critical data gaps in existing datasets and proposes a methodological framework for improving soil mapping practices, thereby contributing to more informed resource management and conservation strategies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".