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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 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)0.004
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.055
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0040.003
Scholarly communication0.0040.003
Open science0.0030.002
Research integrity0.0430.019
Insufficient payload (model declined to judge)0.0550.065

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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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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