Two New Metrics for Evaluating Pixel-Based Change in Data Sets of Global Extent due to Projection Transformation
Bibliographic record
Abstract
New metrics are introduced for tracking pixel loss and duplication during the transformation of discrete raster data sets by map projection. The metrics, PL and PD, successfully measure pixel loss and pixel duplication, respectively, throughout the spatial realm and provide evidence of the property of equal area. PL and PD are applied to the examination of world equal-area map projections. Traditional map projection distortion evaluation addresses scale, area, shape, and directional distortion, which is appropriate to point-by-point analytical projection methods used for vector data. PL and PD provide an additional means for evaluating map projection distortion for discrete raster data. Data producers and data users, including researchers and policy makers, are often unaware that the choice of a map projection may affect the content of data sets and, possibly, research results. Pixel duplication may be reversed in some cases, but lost pixels mean that data has been lost forever. The results of this work indicate the need for a change in cartographic recommendations for selecting global raster data map projections. The Sinusoid projection, with the greatest angular distortion of the projections studied, would be an unlikely choice for a global equal-area data set, but it exhibits no pixel loss or duplication.
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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 | 0.018 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".