Assessing spatial attributes of forest landscape values: an internet-based participatory mapping approach
Bibliographic record
Abstract
We designed and developed an internet mapping application to collect data on the locations of forest landscape values across a 2.4 million hectare study area in the province of Alberta, Canada. Four communities in the study area were surveyed and 8053 point locations were mapped for 10 different value types. Importance weights of landscape values were determined through a ranking exercise. Nearest-neighbour and second-order spatial point pattern analysis (K functions) suggested that all value types were significantly clustered across the study area. Recreational, wilderness, existence, and biological diversity values exhibited maximum clustering at larger spatial scales in comparison with educational, economic, historic or cultural, and spiritual values. Maximum clustering was positively related to mean road density and negatively related to mean distance to water, which suggests that landscape features influence the spatial pattern of values by acting as focal points or attractors for values. An applied use of the data for values hotspot detection and community protection zoning in forest fire management planning is presented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".