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Record W2916186450 · doi:10.1029/2018jc014639

Variability of Surface Water <i>p</i>CO<sub>2</sub> in the Canadian Arctic Archipelago From 2010 to 2016

2019· article· en· W2916186450 on OpenAlexafffundabout
Mohamed Ahmed, Brent Else, Tonya Burgers, Tim Papakyriakou

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of ManitobaUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaNetworks of Centres of Excellence of CanadaUniversity of Calgary
KeywordsUpwellingArcticArchipelagoEnvironmental scienceSea iceOceanographySalinityClimatologySink (geography)Sea surface temperatureSupersaturationSeawaterAtmospheric sciencesArctic ice packGeologyGeographyChemistry

Abstract

fetched live from OpenAlex

Abstract This study provides 6 years of high‐resolution underway measurements of the sea surface partial pressure of CO 2 ( p CO 2sw ), sea surface temperature, and salinity across the Canadian Arctic Archipelago (CAA). Observed p CO 2sw varied regionally, with the northern and central channels of the CAA undersaturated in p CO 2sw (with respect to the atmosphere), while the western regions were typically saturated to supersaturated in p CO 2sw . This apparent spatial variability was caused to some extent by the timing of our ship transit through the CAA, as we also found a general seasonal trend of p CO 2sw being undersaturated in the early summer, followed by saturation to supersaturation in late summer, and a return to undersaturation during the autumn. Sea surface temperature was significantly correlated with p CO 2sw at various locations across the CAA, but we also observed the effects of other regional processes like upwelling, primary production, riverine input, and sea ice melt. These processes are linked to each other, and hence, it is impossible to pinpoint only one dominant factor controlling p CO 2sw variability in the CAA. However, we found that sea ice dominates the seasonal cycle of all these processes, thus making the timing of sea ice breakup a useful predictor of p CO 2sw variability in the CAA. We calculated an average net oceanic sink of 14 mmol CO 2 · m −2 · day −1 for the CAA during the summer and autumn seasons, but caution that a more rigorous budgeting approach is required to fully account for biases in dates and locations of our measurements.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.047
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.018
GPT teacher head0.258
Teacher spread0.240 · 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

Citations43
Published2019
Admission routes3
Has abstractyes

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