Decadal Grid Analysis of Landscape Metrics for Provincial Parks of British Columbia
Bibliographic record
Abstract
Provincial parks of British Columbia are protected for their high ecological, cultural, and recreational values. With climate change exacerbating, more frequent and severe disturbances are anticipated to cause land cover alteration in provincial parks, leading to loss of habitat and ecosystem services. It’s crucial to develop an overarching understanding of how parks’ spatial configuration changed in the past to make informed and proactive management decisions. Using the "landscapemetrics" package in R, the project computed class-level landscape metrics on 1 km by 1 km grids to quantitatively analyze decadal changes in spatial configuration of forests and shrubs in E.C. Manning Park, Mount Robson Park, Tweedsmuir Park, and West Arm Park from 1989 to 2019. The metrics were tested to characterize landscape metrics of the park edge along the boundary versus the park interior. The metrics highlighted the spatial distribution and shape complexity of forests and shrubs and time frames during which noticeable land cover alterations occurred. The results provided spatial, temporal, and statistical insights into the landscape dynamic over the decades and showed that future park management could benefit from accounting for the spatial variation of the landscape configuration within parks by developing area-specific ecosystem restoration strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".