Circumpolar Arctic Man-made Impervious Surface Area (CAMI) Datasets
Bibliographic record
Abstract
Circumpolar Arctic Man-made Impervious Surface Area (CAMI) is a series of fine spatial resolution man-made impervious surface maps covering the entire circumpolar area northward the Arctic treeline. So far, CAMI consists of two individual products: CAMI and CAMI-2020. Both products were generated using the Google Earth Engine platform. The CAMI product contains annual change information of Pan-Arctic impervious surface area from 1999 to 2018 at a 30m resolution. The year of the transition (i.e., from pervious to impervious) is identified from the pixel DN value, ranging from 1999 to 2018. All other DN values represent non-impervious. The CAMI product is generally organized and named as country-specfic rar files in Esri Grid format, with suffixes CA, US, and NE representing Canada, United States of America, and Nordic countries/regions including Norway, Greenland, and Iceland. Due to the large file size, we further divided CAMI within Russia into three parts: RU1, RU2, and RU3. The CAMI-2020 product is an updated version of CAMI that maps Pan-Arctic man-made impervious surfaces at a 10m spatial resolution circa 2020. The product is provided in TIFF format, including six files representing six countries/regions (United States of America, Canada, Greenland, Iceland, Norway and Russia respectively) in the Arctic. Each file was named as "CAMI2020_" plus the countries/region name. The DN value of 8 represents imperviousness, while others are natural land covers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".