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Record W3005406355 · doi:10.1029/2019gl085707

Airborne Mapping Reveals Emergent Power Law of Arctic Methane Emissions

2020· article· en· W3005406355 on OpenAlexaboutno aff
Clayton D. Elder, David R. Thompson, Andrew K. Thorpe, P. Hanke, Katey Walter Anthony, Charles E. Miller

Bibliographic record

VenueGeophysical Research Letters · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsPermafrostEnvironmental scienceArcticHotspot (geology)Greenhouse gasEcotonePhysical geographyAtmospheric sciencesMethaneWetlandPower lawClimatologyGeologyGeographyOceanographyEcology

Abstract

fetched live from OpenAlex

Abstract Methane (CH 4 ) emissions from thawing permafrost amplify a climate warming feedback. However, upscaling of site‐level CH 4 observations across diverse Arctic landscapes remains highly uncertain, compromising accuracy of current pan‐Arctic CH 4 budgets and confidence in model forecasts. We report a 30,000‐km 2 survey at 25‐m 2 resolution (~1 billion observations) of CH 4 hotspot patterns across Alaska and northwestern Canada using airborne imaging spectroscopy. Hotspots covered 0.2% of the surveyed area, concentrated in the wetland‐upland ecotone, and followed a two‐component power law as a function of distance from standing water. Hotspots decreased sharply over the first 40 m from standing water ( y = 0.21× −0.649 , R 2 = 0.97), mirroring in situ flux observations. Beyond 40 m, CH 4 hotspots diminished gradually over hundreds of meters ( y = 0.004× −0.164 , R 2 = 0.99). This emergent property quantifies the distribution of strong methanogenic zones from site to regional scales, vastly improving metrics for scaling ground‐based CH 4 inventories and validation of land models.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.939
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.034
GPT teacher head0.284
Teacher spread0.251 · 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; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations76
Published2020
Admission routes1
Has abstractyes

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