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Comment on egusphere-2023-1446

2023· peer-review· en· W4387670033 on OpenAlexafffundabout
Xin Yang, Kimberly Strong, Alison S. Criscitiello, Marta Santos-Garcia, Kristof Bognar, Xiaoyi Zhao, P. F. Fogal, Kaley A. Walker, Sara Morris, Peter Effertz

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutions3v Geomatics (Canada)University of AlbertaEnvironment and Climate Change CanadaUniversity of Toronto
FundersNatural Environment Research CouncilEurostarsNatural Sciences and Engineering Research Council of CanadaNational Oceanic and Atmospheric AdministrationEnvironment and Climate Change Canada
KeywordsSnowArcticSea iceSalinitySnowpackSea saltBromineSeawaterEnvironmental scienceAtmospheric sciencesSink (geography)ChemistryOceanographyGeologyGeographyMeteorologyGeomorphology

Abstract

fetched live from OpenAlex

Abstract. This study explores the role of snowpack in polar boundary layer chemistry, especially as a direct source of reactive bromine (BrOX=BrO+Br) and nitrogen (NOX=NO+NO2) in the Arctic springtime. Surface snow samples were collected daily from a Canadian high Arctic location at Eureka, Nunavut (80° N, 86° W) from the end of February to the end of March in 2018 and 2019. The snow was sampled at several sites representing distinct environments: sea ice, inland close to sea level, and a hilltop ~600 m above sea level (asl). At the inland sites, surface snow salinity has a double-peak distribution with the first and lowest peak at 0.001–0.002 practical salinity unit (psu), which corresponds to the precipitation effect, and the second peak at 0.01–0.04 psu, which is likely related to the salt accumulation effect (due to loss of water vapour by sublimation). Snow salinity on sea ice has a triple-peak distribution; its first and second peaks overlap with the inland peaks, and the third peak at 0.2–0.4 psu is likely due to the sea water effect (due to upward migration of brine on sea ice). At all sites, snow sodium and chloride concentrations increase by almost 10-fold from the top 0.2 cm to ~1.5 cm in depth. Surface snow bromide at sea level is significantly enriched, indicating a net sink of atmospheric bromine. Moreover, surface snow bromide at sea level has an increasing trend over the measurement time period, with mean slopes of 0.024 in the 0–0.2 cm layer and 0.016 μM d-1 in the 0.2–0.5 cm layer. Surface snow nitrate at sea level also shows a significant increasing trend, with mean slopes of 0.27, 0.20, and 0.07 μM d-1 in the top 0.2 cm, 0.2–0.5 cm, and 0.5–1.5 cm layers, respectively. Using these trends, an integrated net deposition flux of bromide of 1.01×107 molecules cm-2 s-1 and an integrated net deposition flux of nitrate of 2.6×108 molecules cm-2 s-1 were derived. In addition, nitrate and bromide in the morning samples are significantly higher than the afternoon samples, indicating a strong photochemistry effect. However, the mean bromide loss rate (0.027–0.040 μM) is smaller than the nitrate loss rate (0.23–0.362 μM) by an order of magnitude, implying the reactive bromine emission flux from snowpack is significantly smaller than the reactive nitrogen emission flux, which is consistent with the large difference between their derived net deposition fluxes. After considering the photochemical loss effect, the corrected bromide deposition flux at sea level is 2.73×107 molecules cm-2 s-1; for nitrate, the corrected deposition flux is 5.98×108 molecules cm-2 s-1. In addition, the surface snow nitrate and bromide at inland sites were found to be significantly correlated (R=0.48–0.76), and the [NO3-]/[Br-] ratio of 4–7 indicates a possible acceleration effect of reactive bromine in atmospheric NOX-to-nitrate conversion. This is the first time such an effect has been seen in snow chemistry data obtained with a sampling frequency as short as one day.

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)0.011
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: Commentary · Consensus signal: none
Teacher disagreement score0.659
Threshold uncertainty score0.487

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0110.007
Insufficient payload (model declined to judge)0.6590.509

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 head0.067
GPT teacher head0.283
Teacher spread0.216 · 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
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 routes3
Has abstractyes

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