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Supplementary material to "Effects of 20–100 nanometre particles on liquid clouds in the clean summertime Arctic"

2016· preprint· en· W4234277745 on OpenAlexaff
W. R. Leaitch, Alexei Korolev, A. A. Aliabadi, Julia Burkart, Megan D. Willis, Jonathan P. D. Abbatt, Heiko Bozem, Peter Hoor, Franziska Köllner, Johannes Schneider, A. Herber, C. Konrad, R. Brauner

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsUniversity of TorontoEnvironment and Climate Change Canada
Fundersnot available
KeywordsNanometreThe arcticArcticPhysicsEnvironmental scienceAtmospheric sciencesChemistryOceanographyGeologyOptics

Abstract

fetched live from OpenAlex

S1. Instrument calibrations, evaluationsThe calibrations of the UCPC, SMS, UHSAS, CCN and FSSP100 were evaluated in the laboratory prior to integration on the aircraft and again with the instrumentation in the aircraft at Resolute Bay.The results of these evaluations are summarized below: a) In the laboratory, a Gilibrator was used to determine the relevant flow rates were within 5% of their expected values for the UCPC, SMS and UHSAS.Particle sizing was evaluated against nearly monodisperse 80 nm and 200 nm polystyrene latex spheres (PSLs) as well as nearly monodisperse 50 nm and 100 nm particles of ammonium sulfate (AS); the latter were generated from the atomization of solution droplets that were dried and subsequently size-selected with a TSI 3081 Electrostatic Classifier.The classifier output was also sampled with a TSI 3034 SMPS for comparison, which sized to within 2% of the nominal settings of the PSLs and the 3081.The peak sizes measured with the SMS ranged from 2% to 10% higher than the nominal sizes.The UHSAS was evaluated only with the nearly monodisperse AS particles, and its peak sizes were found to be 6%-8% below the nominal sizes.The number concentrations of both the SMS and UHSAS were evaluated against number concentrations measured with a TSI 3772 CPC, and the agreement was to within 7%.b) Particle transmission through the CCN low pressure inlet was assessed using two TSI 3772 CPCs: one placed before the inlet at approximately 995 hPa, and one after the inlet at reduced pressure of 650 hPa.The mean ratio of the reduced pressure CPC to the ambient pressure CPC for nearly monodisperse AS particles at several diameters ranging between 35 nm and 110 nm was 0.67, which compares well with the expected reduction by a factor of approximately 0.65.However, the ratio of the CPCs varied between 0.6 and 0.76 with the higher values at smaller particle sizes (Figure S1).The reason for the variation is unknown, but it suggests that the CCNC concentrations sampled through this low-pressure inlet may be artificially increased for particles <70 nm and reduced for particles >70 nm by up to 8%.

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 categoriesInsufficient payload (model declined to judge)
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 score0.627
Threshold uncertainty score0.532

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.6270.094

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 head0.012
GPT teacher head0.243
Teacher spread0.231 · 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.

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
Published2016
Admission routes1
Has abstractyes

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