Quantifying bias in pattern indices extracted from spatially offset landscape samples
Bibliographic record
Abstract
Modern ecological models often account for the influence of the surrounding environment by using landscape pattern indices (LPIs) as measures of landscape structure. Ideally, the landscape samples from which these LPIs are extracted should be centered on the locations where the response variable was measured. However, in situations where this is not possible due to a lack of adequate full-coverage landcover data, the question arises as to what degree this circumstance creates a bias in the value of the LPIs, thereby obscuring their relation with the response variable. To address this question, we extracted four representative LPIs from 30 rectangular (3 × 6 km) landscape samples evenly distributed across a 10 000 km2 boreal forest study area. These rectangles were subjected to systematic displacements across a range of distances (0.5 to 2.5 km) and directions, after which we recomputed the LPIs. We found that a 1 km spatial offset led to an average of 15% deviation of original LPI values. Unfortunately, as the offset increased, the range of resulting deviations also widened, making it difficult to predict this effect. Our findings fill a gap in the literature on landscape pattern analysis and suggest that researchers should avoid LPIs extracted from spatially offset landscape samples.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".