MétaCan
Menu
Back to cohort
Record W2579362214

The Chemical Composition of High Arctic Snow: Deposition Mechanisms and Sources

2016· dissertation· en· W2579362214 on OpenAlexfundaboutno aff
Katrina M. Macdonald

Bibliographic record

VenueTSpace (University of Toronto) · 2016
Typedissertation
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaEnvironment and Climate Change CanadaGovernment of Ontario
KeywordsSnowArcticDeposition (geology)Chemical compositionComposition (language)Environmental sciencePhysical geographyEarth scienceGeographyOceanographyGeologyMeteorologyChemistryGeomorphologyArt
DOInot available

Abstract

fetched live from OpenAlex

Recent observations of Arctic temperature increases and ice/snow loss have highlighted the importance of defining pollutant pathways to the Arctic. Fresh snow samples collected at Alert, Nunavut, from September 2014 to June 2015 were analyzed for carbon species, major ions, and metals, and their concentrations and fluxes reported. Comparison with simultaneous atmospheric monitoring found dry deposition to be a dominant removal mechanism for several compounds over the winter while wet deposition increased in importance in the fall/spring, possibly due to enhanced scavenging by mixed-phase clouds. This unprecedented dataset provided an opportunity for a temporally-refined source apportionment of key snow impurities. The majority (73%) of the black carbon in snow, a light-absorbing compound critical to the Arctic radiative balance, was identified as the product of fossil fuel burning with limited biomass burning influence. Both depositional and sourcing analyses suggested the external mixing of black carbon, sea salt, crustal, and sulphate aerosols.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
Threshold uncertainty score0.994

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)0.0070.000

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 head0.007
GPT teacher head0.204
Teacher spread0.197 · 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

Citations0
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueTSpace (University of Toronto)Same topicnanoparticles nucleation surface interactionsFrench-language works237,207