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Record W2041825767 · doi:10.1002/hyp.6239

Heat transport in the Red Lake Bog, Glacial Lake Agassiz Peatlands

2006· article· en· W2041825767 on OpenAlexaff
Jeffrey M. McKenzie, Donald I. Siegel, Donald O. Rosenberry, Paul H. Glaser, Clifford I. Voss

Bibliographic record

VenueHydrological Processes · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsMcGill University
FundersSyracuse UniversityNational Science Foundation
KeywordsPeatBogAdvectionEnvironmental scienceHydrology (agriculture)GeologyGlacial periodThermal conductionAtmospheric sciencesGeomorphologyGeotechnical engineeringMaterials scienceGeography

Abstract

fetched live from OpenAlex

Abstract We report the results of an investigation on the processes controlling heat transport in peat under a large bog in the Glacial Lake Agassiz Peatlands. For 2 years, starting in July 1998, we recorded temperature at 12 depth intervals from 0 to 400 cm within a vertical peat profile at the crest of the bog at sub‐daily intervals. We also recorded air temperature 1 m above the peat surface. We calculate a peat thermal conductivity of 0·5 W m −1 °C −1 and model vertical heat transport through the peat using the SUTRA model. The model was calibrated to the first year of data, and then evaluated against the second year of collected heat data. The model results suggest that advective pore‐water flow is not necessary to transport heat within the peat profile and most of the heat is transferred by thermal conduction alone in these waterlogged soils. In the spring season, a zero‐curtain effect controls the transport of heat through shallow depths of the peat. Changes in local climate and the resulting changes in thermal transport still may cause non‐linear feedbacks in methane emissions related to the generation of methane deeper within the peat profile as regional temperatures increase. Copyright © 2006 John Wiley & Sons, Ltd.

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 score0.162
Threshold uncertainty score0.998

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)0.0030.000

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 head0.010
GPT teacher head0.215
Teacher spread0.204 · 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

Citations43
Published2006
Admission routes1
Has abstractyes

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