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Record W3084529454 · doi:10.1594/pangaea.905912

Geochemistry and greenhouse gas production of incubated permafrost and seawater from the western Canadian Arctic

2019· dataset· en· W3084529454 on OpenAlexaboutno aff
George Tanski, Dirk Wagner, Christian Knoblauch, Michael Fritz, Torsten Sachs, Hugues Lantuit

Bibliographic record

VenueFigshare · 2019
Typedataset
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsPermafrostSeawaterArcticEnvironmental scienceGreenhouse gasGeochemistryOceanographyEarth sciencePhysical geographyGeologyEnvironmental chemistryChemistryGeography

Abstract

fetched live from OpenAlex

The data set includes geochemical and hydrochemical information on individual permafrost and seawater samples from the Yukon Coast in the western Canadian Arctic used for an incubation experiment. The experiment mimicked erosion of permafrost coasts in the Arctic by mixing different kind of permafrost (i.e. organic-enriched and mineral) with ambient seawater at temperatures of 4 and 16°C during the course of 4 months (the approximate length of an Arctic open-water season). The data sets contain information on basic geochemical parameters measured before and after the experiment and on the production of greenhouse gases, which include carbon dioxide (CO2) and methane (CH4). The first data set contains the geochemical and hydrochemical data for permafrost and seawater as well as the total amount of CO2 and CH4 cumulated during the course of the experiment. The second data set contains information on the individual measurements of CO2 and CH4 (in ppm) with a gas chromatograph. The study aims at understanding the role of eroding permafrost coasts for the Arctic carbon cycle and budget.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.066
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.013
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0030.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0070.006

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 head0.016
GPT teacher head0.207
Teacher spread0.191 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2019
Admission routes1
Has abstractyes

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