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Record W2183656168

Effects of Long-Term History on Borehole Paleoclimatology

2011· article· en· W2183656168 on OpenAlexaffabout
Gurpreet S. Matharoo, Hugo Beltrami, Volker Rath

Bibliographic record

VenueEGU General Assembly Conference Abstracts · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsGeologyBoreholeGeothermal gradientContext (archaeology)ClimatologyIce sheetGeophysicsGlacial periodGeomorphologyPaleontology
DOInot available

Abstract

fetched live from OpenAlex

Temperature changes at the surface of the earth propagate downward into the earth and are stored as perturbations to the subsurface equilibrium thermal regime associated with heat flow from the interior of the earth. Within the context of Borehole climatology, a methodology that has been widely used to reconstruct past ground surface temperature histories (GSTH), we examine the effect on the reconstructed GSTH arising from the changes that took place up to 120 kyrs before the present. The changes include the presence of an extended ice sheet, the duration of this cover, and the basal conditions of the ice sheet to account for the climatological interpretation of borehole temperature profiles. We choose three different sites from Canada. For each site, we have extracted 120 kyrs to present ground surface temperature time-series for a range of ice-cover histories from a data-calibrated glacial systems model for the last North American ice complex. These time-series provide a transient upper boundary condition for a 1-D subsurface heat transport model. This forcing introduces a thermal anomaly on a synthetic steady-state geothermal regime with equilibrium surface temperature of 8.0 C and geothermal gradient of 20 K km 1 under the assumption of an homogeneous subsurface. The corresponding results are sampled for depths of 600 m and 1000 m and are used to reconstruct the climateinduced subsurface anomalies. These anomaly profiles indicate that the long-term history has an effect that is visible at both depths but the effect is prominent at larger depths. For each site, we also use a series of 1000 MonteCarlo experiments where Gaussian noise with zero mean and standard deviation of 0.5 K is added to the forcing time series in order to give an estimate of the range of variability of the subsurface anomalies.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.049
GPT teacher head0.225
Teacher spread0.176 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2011
Admission routes2
Has abstractyes

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