Bibliographic record
Abstract
Models for the prediction of soil texture for the Abitibi River Forest (ARF) region in the District of Cochrane in Ontario, Canada were created from environmental covariates generated from remotely-sensed data as soil formation factors. A novel approach of incorporating LiDAR (Light Detection and Ranging) retrievals for the entire study area to derive covariates of canopy height model (CHM) and gap fraction was investigated. CHM and gap fraction had high variable importance for the soil texture models fitted for the region, with CHM being the most important variable out of a set of 104 predictors, and gap fraction among the top predictors. Random forest (RF) and support vector machine with radial basis functions (SVM Radial) approaches were utilized for the soil texture classification. The inclusion of CHM and gap fraction with other environmental predictors improved upon the accuracy of soil texture models, with accuracy scores exceeding 0.7 and Cohen's kappa greater than 0.5. Prediction maps for soil texture were generated for the ARF study region.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".