Using geographic information systems to make transparent and weighted decisions on pit development: incorporation of interactive economic, environmental, and social factors
Bibliographic record
Abstract
A geographic information systems platform with an analytical hierarchy process was employed to rank the importance of different economic, environmental, and social factors involved in choosing the location of an open-pit operation within a small county in the province of Ontario, Canada. Weighted environmental (hydraulic conductivity, soil types, slope, and elevation) and social (distance from population zones) overlays were combined and then compared against a map of potential sources of sand and gravel deposits (economic factor) to locate the most ideal location for a pit. This resulted in the delineation of four ideal locations for the operation in the north of the county. Here, permeability values are low and there are no major population centres. The decision-making tool developed here has the ability to adapt to changing social and (or) environmental criteria and could greatly improve transparency in natural resource management decisions. The largest limitation to this decision-making tool is that it treats all water sources as equal. As research continues to identify different ecosystem services (i.e., acid neutralization, low contamination source waters, and high biological diversity) for different types of waterways, a ranking scheme could be added along the lines of high versus low conservation priorities for nonrenewable freshwater lake and river resources.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".