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Record W3117367642

Black Carbon Measurements of Snow and Ice Using the Single Particle Soot Photometer: Method Development and an AD 1852-1999 Record of Atmospheric Black Carbon from a Mount Logan Ice Core

2013· article· en· W3117367642 on OpenAlexaboutno aff
J. A. Menking

Bibliographic record

VenueScholarWorks (Central Washington University) · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsCarbon blackSootIce coreSnowClimatologyAtmospheric sciencesGeologyEnvironmental sciencePhotometerCarbon fibersMeteorologyGeographyGeomorphologyPhysicsMaterials scienceAstronomyChemistry
DOInot available

Abstract

fetched live from OpenAlex

Black carbon (BC), produced by the combustion of fossil and biofuels, warms the climate by absorbing solar radiation when in the atmosphere and by reducing the albedo of snow and ice when deposited. Measuring BC in snow and ice is important for estimating albedo reduction and developing historical records of BC concentration. Experiments were conducted to further develop a method for measuring BC in snow and ice using the Single Particle Soot Photometer (SP2). Results suggest the optimal procedures for sample storage, treatment, and nebulization, and analysis and calibration of BC concentrations measured using the SP2 coupled to a CETAC ultrasonic nebulizer. The methods were then used to develop an AD 1852-1999 record of BC using an ice core from Mt. Logan in the Yukon Territory, Canada. The BC recorded at Mt. Logan is predominantly from biomass burning in Alaska, the Yukon Territory, and Siberia. Climatic implications of the BC record are discussed.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.920
Threshold uncertainty score0.160

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.220
Teacher spread0.192 · 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

Citations3
Published2013
Admission routes1
Has abstractyes

Explore more

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