MétaCan
Menu
Back to cohort
Record W1987153639 · doi:10.3155/1047-3289.60.9.1065

Buoyancy-Corrected Gravimetric Analysis of Lightly Loaded Filters

2010· article· en· W1987153639 on OpenAlexaff
Pat E. Rasmussen, H. David Gardner, Jianjun Niu

Bibliographic record

VenueJournal of the Air & Waste Management Association · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsUniversity of OttawaHealth Canada
Fundersnot available
KeywordsGravimetric analysisRelative humidityBuoyancyHumidityAnalytical Chemistry (journal)Air conditioningParticle sizeAbsorption (acoustics)Body orificeChemistryMaterials scienceEnvironmental scienceChromatographyMeteorologyComposite materialThermodynamics

Abstract

fetched live from OpenAlex

Numerous sources of uncertainty are associated with the gravimetric analysis of lightly loaded air filter samples (< 100 microg). The purpose of the study presented here is to investigate the effectiveness and limitations of air buoyancy corrections over experimentally adjusted conditions of temperature (21-25 degrees C) and relative humidity (RH) (16-60% RH). Conditioning (24 hr) and weighing were performed inside the Archimedes M3 environmentally controlled chamber. The measurements were performed using 20 size-fractionated samples of resuspended house dust loaded onto Teflo (PTFE) filters using a Micro-Orifice Uniform Deposit Impactor representing a wide range of mass loading (7.2-3130 microg) and cut sizes (0.056-9.9 microm). By maintaining tight controls on humidity (within 0.5% RH of control setting) throughout pre- and postweighing at each stepwise increase in RH, it was possible to quantify error due to water absorption: 45% of the total mass change due to water absorption occurred between 16 and 50% RH, and 55% occurred between 50 and 60% RH. The buoyancy corrections ranged from -3.5 to +5.8 microg in magnitude and improved relative standard deviation (RSD) from 21.3% (uncorrected) to 5.6% (corrected) for a 7.2 microg sample. It is recommended that protocols for weighing low-mass particle samples (e.g., nanoparticle samples) should include buoyancy corrections and tight temperature/humidity controls. In some cases, conditioning times longer than 24 hr may be warranted.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.072
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.004
GPT teacher head0.194
Teacher spread0.190 · 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.

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

Citations22
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the Air & Waste Management AssociationSame topicWind and Air Flow StudiesFrench-language works237,207