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Record W4313561795 · doi:10.5194/acp-2022-668-rc2

Comment on acp-2022-668

2023· peer-review· en· W4313561795 on OpenAlexaff

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsTrent UniversityEnvironment and Climate Change CanadaYork University
FundersUniversity of Houston
KeywordsDeposition (geology)Environmental scienceFlux (metallurgy)Atmospheric sciencesAtmosphere (unit)TaigaBorealWind speedMeteorologyTowerChemistryGeographyForestryPhysicsGeology

Abstract

fetched live from OpenAlex

The emission of SO2 from the Athabasca oil sands region (AOSR) has been shown to impact the surrounding forest area and human exposure. Recent studies using aircraft-based measurements have demonstrated that deposition of SO2 to the forest is at a rate many times higher than model estimates. Here we use the flux/gradient method to estimate SO2 deposition rates at two tower sites in the boreal forest downwind of AOSR SO2 emissions. We use both continuous and passive sampler measurements and compare both techniques. The measurements predict SO2 deposition velocities ranging from 2.1–5.9 cm s-1. There are uncertainties associated with the passive sampler flux/gradient analysis, primarily due to an assumed Schmidt number, a required assumption of independent variables, and potential wind effects. We estimate the total uncertainty as ±2 cm s-1. Accounting for these uncertainties, the measurements are near (or slightly higher than) the previous aircraft-based measurements (1.2–3.2 cm s-1) and significantly higher than model estimates for the same measurement periods (0.1–0.6 cm s-1), suggesting that SO2 has a much shorter lifetime in the atmosphere than is currently predicted by models.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.311
Threshold uncertainty score0.997

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.004

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 head0.038
GPT teacher head0.294
Teacher spread0.256 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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