MétaCan
Menu
Back to cohort
Record W6963483938 · doi:10.18739/a29w09052

Composition of aerosol in airborne particulate matter and snow at Alert, Canada 2014-2015

2020· dataset· en· W6963483938 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCalifornia Digital Library · 2020
Typedataset
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsAerosolParticulatesSnowAtmosphere (unit)Carbon fibersCombustionChemical compositionTotal organic carbon

Abstract

fetched live from OpenAlex

Carbonaceous aerosols are a major component of fine airborne particulate matter (PM) and play a complex role in the climate system, via their role in light scattering and absorption, cloud nucleation, and the melting of ice- and snow-covered surfaces, and in air pollution and human health. They are removed from the atmosphere via aging and dry and wet deposition. Over the course of one year, we simultaneously analyzed the composition of carbonaceous aerosol in both PM and snow collected at the Dr. Neil Trivett Global Atmosphere Watch Observatory at Alert, Canada. To understand the seasonal variation in the EC (elemental carbon) and OC (organic carbon) burden, we quantified the amount of total carbon (TC) and fraction of light-scattering organic carbon (OC) and light-absorbing elemental carbon (EC) with a Sunset OC/EC analyzer using the EnCan-Total-900 (ECT9) protocol. In addition, we measured the stable carbon value and radiocarbon content of the EC fraction to apportion it into contributions from fossil fuel combustion (gaseous, liquid, and solid fuels such as natural gas, coal, and diesel) and biomass burning (wildfires and biofuel combustion).

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.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.049
Threshold uncertainty score1.000

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)0.0050.001

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 head0.005
GPT teacher head0.167
Teacher spread0.161 · 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