ASSESSING VISUAL PREFERENCE FOR AGGREGATE PIT REHABILITATION DESIGNS USING COMPUTER ANIMATED LANDSCAPE MODELS
Bibliographic record
Abstract
The most common concern expressed by the public regarding aggregate mining is the negative aesthetic impact the activity has upon the scenic quality of the landscape. It is important that rehabilitation efforts restore scenic quality as well as function to the post-mining landscape. To this end the Management of Abandoned Aggregate Properties (MAAP) Program rehabilitates ten to twenty-fives sites in Ontario annually, based upon the criteria of safety, aesthetic, ecological and economic concerns. This study assesses the public's visual preference for different aggregate property naturalization designs. To investigate this relationship, computer-modeled rehabilitation designs of an aggregate property were developed using landscape modeling software to simulate three-dimensional development over a fifteen year time period. Respondent groups evaluated how natural, rehabilitated, attractive they perceived the simulations to be and assigned a rank order to the eight design strategies from best to worst. Design alternatives are shown to improve the perception of aggregate rehabilitation efforts over the current methods being commonly employed by MAAP.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".