GEOPROCESSING SOLUTIONS DEVELOPED WHILE CALCULATING HUMAN FOOTPRINTTM STATISTICS FOR ZONES REPRESENTING PROTECTED AREAS AND ADJACENT LANDS AT THE CONTINENT SCALE
Bibliographic record
Abstract
We calculated the mean Human Footprint TM (HF) for 196,498 polygons representing state and federally administrated areas (e.g., National Forests, National Parks, State and Provincial Parks, etc.) of Canada, Mexico, and the Continental United States. Separate sets of calculations were made for (1) the area in each protected area which ranged in size from less than one to over 11 million hectares and (2) the area outside and within 10 km of each protected area. We used Last of the Wild version 2 (2005) for North America as the source of data for HF values. This paper concerns the technical problems we encountered using ArcGIS 9.3 and Spatial Analyst to accomplish this task in a timely manner. We developed several scripts to automate processes and address overlapping polygons resulting from zone calculations of 10 km around each protected area (doughnut-shaped polygons defining the zones from which to calculate average HF values adjacent to protected areas). We learned that Spatial Analyst does not honor the object integrity of overlapping polygons when using them to define zones for calculating zonal statistics from a raster database. We tried alternative solutions, including the use of Hawth's Analysis Tools version 3.27 (Zonal Statistics ++) and writing scripts in Visual Basic 6.0 (VBA) to separate overlapping polygons and to calculate zonal statistics both as a table and output raster database. One of the four scripts resulting from this project was developed to calculate the 10 km zone around each protected area polygon. This script can be used to calculate a separate 'doughnut' polygon for any distance outside of any size polygon, even if it shares boundaries with other polygons. We also discovered that the Zonal Statistics function in Spatial Analyst does not calculate all of the zones in a large database even if the polygons do not overlap. Our solution for this problem is described in this paper as an iterative process ending with another custom script to define the raster value located under the label point of each polygon in a vector database. Ultimately, we successfully calculated the mean HF from a spatially defined raster database both inside and outside the nearly 200,000 polygons defining the boundaries of Protected Areas in North America ( http://cec.org/atlas ).
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.019 |
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".