SMART – Space monitoring of Arctic Tundra landscapes
Bibliographic record
Abstract
Satellite remote sensing as well as in-situ measurements are common tools to monitor the state of Arctic environments. However, remote sensing products often lack sufficient temporal and/or spatial resolution, and in-situ measurements can only describe the environmental conditions on a very limited spatial scale. Therefore, we conducted an air-borne campaign to connect the detailed in-situ data with poor spatial coverage to coarse satellite images. The SMART campaign is part of the ongoing project „Characterization of Polar Permafrost Landscapes by Means of Multi-Temporal and Multi-Scale Remote Sensing, and In-Situ Measurements“, funded by the German Research Foundation (DFG). The focus of the project is to close the gap between in-situ measurements and space-borne images in polar permafrost landscapes. The airborne campaign SMART was conducted in late summer 2018 in north-west Canada, focussing on the Mackenzie-Delta region, which is underlain by permafrost and rarely inhabited. The land cover is either dominated by open Tundra landscapes or by boreal forests. The Polar-5 research-aircraft from the Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research, Germany, was equipped with a ground penetrating radar, a hyperspectral camara, a laserscanner, and an infrared temperature sensor amongst others. In parallel to the airborne acquisition, a team collected in-situ data on ground, including manual active layer depth measurements, geophysical surveying using 2D Electric Resistivity Tomography (ERT), GPR, and mapping of additional land cover properties. The database was completed by a variety of satellite data from different platforms, e.g. MODIS, Landsat, TerraSAR-X and Sentinel-1. As part of the project, we analysed the performance of MODIS Land surfaces temperature products compared to our air-borne infrared measurements and evaluated, how long the land surface temperatures of this Arctic environment can be considered as stable. It turned out that the MODIS data differ up to 2°C from the air-borne measurements. If this is due to the spatial difference of the measurements or a result of data processing of the MODIS LST products is part of ongoing analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".