The Oak Ridges Moraine in the Greater Toronto
Bibliographic record
Abstract
training point set to evaluate the potential distribution of artesian aquifers and their spatial associations with other geological and topological features in the study area. Evidential layers of geological and topographical features were constructed on the basis of the digital elevation model (DEM) and a geological map using GIS buffering functions in conjunction with weights of evidence method. It has been demonstrated that the locations of the flowing wells in the Oak Ridges Moraine area are associated spatially with the distances, (a) 500–5000 m from the oak ridges moraine deposits, (b) 500–4000 m from thick drift layer delineated on the drift thickness map created from water well data, and (c) 1500–2500 m from steep slope zones with slope above 8 degree calculated from a DEM. Applying a combination of these conditions can reduce the predicting target areas of having flowing wells by two thirds. Outcomes of this research are important both because the impact of the results on understanding of characteristics of aquifers and their relationships with other geological and topographical features and because it generates a probability map showing the potential location of artesian aquifers in the ORM area. In addition, the methodologies used in the paper will be applicable for modeling the distributions of other types of objects such as surface water bodies and low flow of streams in a watershed context in the study area.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".