Effects of cell sizes on resistance surfaces in GIS-based cost distance modeling for landscape analyses
Bibliographic record
Abstract
GIS-based cost distance modeling has been increasingly applied to evaluate habitat quality of existing landscapes and to test "what-if" scenarios for habitat restoration planning. The validity of the raster-based modeling tool is inevitably affected by the cell size of the modeling input. Such effects may be assessed at two levels: resistance and accumulated cost surfaces. This study assesses the resistance-level effects by scaling-up a base landscape at 25 m resolution with six aggregation approaches: the nearest-neighbor assignment, the bilinear interpolation, the cubic convolution, the BlockMajority, the BlockMean and the AggregateMean in ArcGISTM. The effects were measured by the difference between the aggregated and base resistance surfaces using the proposed measures: the mean absolute error, the mean squared error and the autocorrelation coefficient. The results showed that increasing aggregation sizes would generally increase the difference between the generated and the base resistance surfaces for all aggregations. Furthermore, the BlockMajority performed best in terms of the first two measures, whereas the BlockMean and AggregateMean performed best if judged by the third measure.
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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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".