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Record W7025107530

UndercoverEisAgents - Bird's Eye View of Permafrost Thawing

2022· other· en· W7025107530 on OpenAlexaboutno aff

Bibliographic record

Venueelib (German Aerospace Center) · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsPermafrostArcticBaseline (sea)Climate changeLivelihoodVegetation (pathology)RadarPaceSatelliteEarth observation
DOInot available

Abstract

fetched live from OpenAlex

People in the Arctic have been experiencing dramatic changes to their landscapes for several decades. One cause is the thawing of permafrost, which affects the livelihoods of indigenous people the hardest. The thawing process of permafrost is also associated with ecological impacts including the release of greenhouse gases.
\nThawing is evident from very small-scale changes and disturbances to the land surface, which have been inadequately documented. By combining the knowledge of the Indigenous people in Northwest Canada and science, we seek to thoroughly understand and monitor surface changes attributable to permafrost thaw. The goal is to improve the database through UAV (Unmanned aerial vehicle) and satellite imagery together with young Citizen Scientists from schools in Northwest Canada and Germany. The high-resolution UAV datasets can also be utilized as a ground truthing baseline for further analyses employing optical and radar remote sensing time series data to gain a better understanding of the long-term changes in the region. This approach allows for the expansion of spaceborne remote sensing to very inaccessible regions in the global north while maintaining knowledge of the conditions on the ground. Due to the planned acquisition period of multiple years as well as the fast pace of changing environments on the ground, a change detection is possible within a short time period. Because one of the main goals of this project is the employment of cost-efficient consumer-grade UAVs, flight parameters must be optimized to enable precise 3D-models created by SfM (Structure from Motion), which are comparable over time as well as consistent with the spaceborne remote sensing datasets. Permafrost soil oftentimes stands out due to its striking polygonal surface features, especially if degradation processes have already set in. These structures range over different spatial scales and can be utilized to determine the grade of degradation. Using very high-resolution imagery of UAVs combined with high temporal repeat acquisitions of satellite remote sensing platforms, a comprehensive archive of observable surface features indicating the degree of degradation can be developed. For this, an automated workflow is going to be implemented, deriving the surface features from the acquired datasets with a subsequent analysis and monitoring of permafrost degradation based on classical image processing approaches as well as KI-based classification methods.
\nIn support of these methods, citizen scientist are integrated in the classification and evaluation process. To this end, school classes from both countries will participate in "virtual shared classrooms" to collect and analyze high-resolution remote sensing data. Students in Germany will be able to gain a direct connection to Northwest Canada through data and knowledge exchange with class mentors. The goals are to transfer knowledge and raise awareness about global warming, permafrost, and related regional and global challenges. The scientific data will provide new insights into biophysical processes in Arctic regions and contribute to a large-scale understanding of the state and change of permafrost in the Arctic.
\nThe UndercoverIceAgents project, funded by the Federal Ministry of Education and Research in Germany, was initiated in summer 2021.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.096
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1180.022

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.

Opus teacher head0.017
GPT teacher head0.288
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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