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Record W4225429169 · doi:10.14430/arctic75150

Modelling Tundra Ponds as Initiators of Peat Plateau Thaw, Northern Hudson Bay Lowland, Manitoba

2022· article· en· W4225429169 on OpenAlexvenueaboutno aff
L D Dyke, W E Sladen

Bibliographic record

VenueARCTIC · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsPeatPermafrostTundraSnowPlateau (mathematics)Environmental scienceSubaerialPhysical geographyBayGeologyGreenhouse gasThermokarstHydrology (agriculture)ArcticGeomorphologyOceanographyGeochemistryGeographyGeotechnical engineeringArchaeology

Abstract

fetched live from OpenAlex

Frozen peat in permafrost regions poses a potential source of increased greenhouse gas production should these deposits thaw. Ponds on frozen peat plateaus in northern Manitoba are numerically modelled as heat sources to determine their potential to promote thaw. Modelling indicates that anticipated climate warming of approximately 2 °C between 2020 and 2050 will produce taliks up to few metres thick beneath ponds a few tens of metres across. However, active-layer thickness in the subaerial parts of peat plateaus will not increase beyond the peat thickness. These findings assume 1) a climate warming rate under a moderately effective intervention in greenhouse gas production, 2) pond freezing regimes that represent both rapid ice formation and ice formation delayed by rapid snow accumulation and 3) snow thermal conductivities that anticipate snow conductivity increase during the freeze interval. These conditions and properties may turn out to be less conducive to talik expansion than the values that will actually occur. Despite these uncertainties, peat plateau pond sizes and plateau margin positions can be monitored to ascertain the onset of accelerated thawing.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.456
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0190.000

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.

Opus teacher head0.040
GPT teacher head0.223
Teacher spread0.183 · 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 designObservational
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

Citations2
Published2022
Admission routes2
Has abstractyes

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Same venueARCTICSame topicClimate change and permafrostFrench-language works237,207