Bibliographic record
Abstract
Mapping surface soil properties and estimating soil parameters with multiresolution data has been significantly advanced by newly developed multiscale mapping technologies, which incorporate the concept and models of scaling analysis in data processing. This study was conducted to develop a new multiscale mapping technique on the basis of a power‐law model characterizing local singularity of exploratory data for mapping surface soil properties. A field with singularity due to self‐organization or self‐similarity properties of the underlying processes can be modeled by multifractal models. These types of data may not have the statistical stationary property required by ordinary geostatistical mapping techniques. The new mapping technique utilizes a scaling property for data interpolation and for downscaling image processing. The inputs, either point data or an image, can be separated into a nonsingular background component for estimation purposes and an anomalous component of singularity for multiscale high‐pass filtering purposes. When used for the purpose of data interpolation, this new method assigns weights for data interpolation by taking into account not only the distance between neighborhood points but also local structures and singularity of the field. The results of application of the method to a data set of geochemical concentration values of Ag from 1172 lake sediments in the Gowganda area of Ontario, Canada, have delineated favorable target areas with strong singularity of Ag concentrations caused by mineralization in lake sediments.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".