Circumpolar Landscape Units, links to GeoTIFFs
Bibliographic record
Abstract
Requirements for space based monitoring of permafrost features had been already defined within the IGOS Cryosphere Theme Report at the start of the IPY in 2007 (IGOS, 2007). The WMO Polar Space Task Group (PSTG, http://www.wmo.int/pages/prog/sat/pstg_en.php) identified the need to review the requirements for permafrost monitoring and to update these requirements in 2013.Relevant surveys with focus on satellite data are already available from the ESA DUE Permafrost User requirements survey (2009), the United States National Research Council (2014) and the ESA – CliC – IPA – GTN –P workshop in February 2014. These reports have been reviewed and specific needs discussed within the community and a white paper submitted to the WMO PSTG. Acquisition requirements for monitoring of especially terrain changes (incl. rock glaciers and coastal erosion) and lakes (extent, ice properties etc.) with respect to current satellite missions have been specified.About 50 locations ('cold spots') where permafrost (Arctic and Antarctic) in situ monitoring has been taking place for many years or where field stations are currently established have been identified. These sites have been proposed to the WMO Polar Space Task Group as focus areas for future monitoring by high resolution satellite data. The specifications of these sites including meta-data on site instrumentation have been published as supplement to the white paper (Bartsch et al. 2014, doi:10.1594/PANGAEA.847003).The representativity of the 'cold spots' around the arctic has been in the following assessed based on a landscape units product which has been developed as part of the FP7 project PAGE21. The ESA DUE Permafrost service has been utilized to produce a pan-arctic database (25km, 2000-2014) comprising Mean Annual Surface Temperature, Annual and summer Amplitude of Surface Temperature, Mean Summer (July-August) Surface Temperature. Surface status (frozen/unfrozen) related products have been also derived from the ESA DUE Permafrost service. This includes the length of unfrozen period, first unfrozen day and first frozen day. In addition, SAR (ENVISAT ASAR GM) statistics as well as topographic parameters have been considered. The circumpolar datasets have been assessed for their redundancy in information content. 12 distinct units could be derived. The landscape units reveal similarities between North Slope Alaska and the region from the Yamal Peninsula to the Yenisei estuary. Northern Canada is characterized by the same landscape units like western Siberia. North-eastern Canada shows similarities to the Laptev coast region. This paper presents the result of this assessment and formulates recommendations for extensions of the in situ monitoring networks and categorizes the sites by satellite data requirements (specifically Sentinels) with respect to the landscape type and related processes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.488 | 0.389 |
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".