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Record W4407228437 · doi:10.1038/s41598-025-88241-8

Variability in storm season intensity modulates ocean acidification conditions in the northern Strait of Georgia

2025· article· en· W4407228437 on OpenAlexafffund
Wiley Evans, Katie Campbell, Carrie Weekes, Justin Del Bel Belluz, Jessy Barrette, Drew Jordison, Jonathan A. Bergshoeff, C. R. M. Prentice, Isabelle Desmarais, Bryn Fedje, E. K. Myers, Alex Hare, Ian Giesbrecht, Colleen T. E. Kellogg, Zoe O. Sandwith, Jennifer M. Jackson

Bibliographic record

VenueScientific Reports · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsFisheries and Oceans CanadaObservatoire global du Saint-Laurent (OGSL)Tula Foundation
FundersFisheries and Oceans CanadaTula Foundation
KeywordsStormOcean acidificationOceanographyEnvironmental scienceIntensity (physics)ClimatologyGeologyClimate change

Abstract

fetched live from OpenAlex

Large changes in marine CO 2 chemistry manifest in areas with weakly-buffered seawater where ocean acidification (OA) acts in concert with natural CO 2 additions. These settings can exhibit periods of extreme OA in the form of multiple co-occurring stressors, including calcite undersaturation and low pH. Such conditions were observed in the northern Strait of Georgia, on the northeast Pacific coast, where extreme OA spanned a 3-year period. Here, we utilized an 8-year, highly-resolved record of seawater CO 2 partial pressure and total dissolved inorganic carbon to decompose the drivers of this extreme OA. We find that variability in storm season intensity shaped the extent of conservative mixing and biogeochemical drivers such that manifests of extreme OA arise in this setting. Extreme OA manifested during years with weak storm seasons due to direct and indirect biogeochemical factors and the reduced impact of conservative mixing. This sensitivity to the storm season intensity highlights how vulnerable the northern Strait of Georgia is to subtle changes in environmental forcing and provides some predictive capacity for OA conditions over the coming year. These results illustrate that OA is not a “slow burn” process within weakly-buffered settings, but rather invokes periods of intensification with poorly understood biological implications.

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)0.005
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 score0.010
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.233
Teacher spread0.223 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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