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Record W4205683175 · doi:10.1029/2021gl095579

Using End‐Member Models to Estimate Seasonal Carbonate Chemistry and Acidification Sensitivity in Temperate Estuaries

2022· article· en· W4205683175 on OpenAlexafffund
Eleanor Simpson, Debby Ianson, Karen E. Kohfeld

Bibliographic record

VenueGeophysical Research Letters · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsFisheries and Oceans CanadaSimon Fraser University
FundersMarine Environmental Observation Prediction and Response Network
KeywordsAlkalinityTemperate climateCarbonateTotal inorganic carbonProductivityOcean acidificationEstuaryEnvironmental scienceCarbon fibersCalcium carbonateEnvironmental chemistryOceanographyChemistryCarbon dioxideEcologyGeologySeawaterBiologyMaterials science

Abstract

fetched live from OpenAlex

Abstract We measured the carbonate system (between 2015 and 2018) in an isolated and a well‐connected temperate estuary, both known for shellfish growth. We evaluated end‐member model estimates of inorganic carbon, alkalinity, pH, mineral saturation states (Ω a ), and pH sensitivity (βDIC). We find winter conditions are estimated within observational uncertainty. Spring‐summer primary productivity elevates observed pH and Ω a above theoretical lines, beyond uncertainty. Both estuaries are sensitive in winter and likely to experience rapid pH changes with increased inorganic carbon inputs. Summer pH sensitivity is reduced by productivity and is least sensitive in the midsalinity region. We estimate carbon increased by up to 49 μmol kg −1 , since the pre‐industrial period resulting in significant decreases in pH (0.2) and Ω a (0.5). The largest pH decrease occurred outside the minimum buffer zone, at higher salinities where carbon increase was greatest. The largest pH decrease occurred in winter, but the largest Ω a decrease occured in summer.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score0.466

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.064
GPT teacher head0.324
Teacher spread0.260 · 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 designSimulation or modeling
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

Citations24
Published2022
Admission routes2
Has abstractyes

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