Quantifying the effects of harvest block design on aesthetic preferences
Bibliographic record
Abstract
In visually sensitive areas, planners use a variety of techniques to mitigate the visible effects of harvesting, including creating designs that appear more natural and less blocky. However, little empirical evidence exists about the degree to which these shapes influence preferences. We aim to fill this gap by investigating the perceptual effects of three shape characteristics: geometric primitive (circle, square, triangle, and trapezoid), complexity, and aspect ratio. Fifty-two photo-realistic images were rendered of a forest scene with a single harvest on a hillside. Forty individuals rated each image. The results indicate that geometric primitive had the largest effect on preference for harvest design of the three variables tested followed very closely by complexity. Yet, the most intriguing finding was the interaction of these two variables. In general, an increase in complexity for square and circular shapes caused higher preference ratings, while for trapezoid and triangle, this was only true as complexity progressed from low to moderate levels. The relationship between preference rating and complexity was nonlinear; the largest improvement existed at the moderate level of complexity. Operationally, this demonstrates that rounded-edged circular shapes are the most preferable but that even with a moderate level of complexity, preferences increase dramatically.
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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 | 0.002 | 0.011 |
| 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) | 0.003 | 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".