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Record W2154852126 · doi:10.5194/os-7-1-2011

Absolute Salinity, ''Density Salinity'' and the Reference-Composition Salinity Scale: present and future use in the seawater standard TEOS-10

2011· article· en· W2154852126 on OpenAlexaff
Daniel G. Wright, Rich Pawlowicz, Trevor J. McDougall, Rainer Feistel, G. M. Marion

Bibliographic record

VenueOcean science · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversity of British ColumbiaBedford Institute of Oceanography
Fundersnot available
KeywordsSalinitySeawaterTemperature salinity diagramsComposition (language)Environmental scienceGeologyOceanography

Abstract

fetched live from OpenAlex

Abstract. Salinity plays a key role in the determination of the thermodynamic properties of seawater and the new TEOS-101 standard provides a consistent and effective approach to dealing with relationships between salinity and these thermodynamic properties. However, there are a number of practical issues that arise in the application of TEOS-10, both in terms of accuracy and scope, including its use in the reduction of field data and in numerical models. First, in the TEOS-10 formulation for IAPSO Standard Seawater, the Gibbs function takes the Reference Salinity as its salinity argument, denoted SR, which provides a measure of the mass fraction of dissolved material in solution based on the Reference Composition approximation for Standard Seawater. We discuss uncertainties in both the Reference Composition and the Reference-Composition Salinity Scale on which Reference Salinity is reported. The Reference Composition provides a much-needed fixed benchmark but modified reference states will inevitably be required to improve the representation of Standard Seawater for some studies. However, the Reference-Composition Salinity Scale should remain unaltered to provide a stable representation of salinity for use with the TEOS-10 Gibbs function and in climate change detection studies. Second, when composition anomalies are present in seawater, no single salinity variable can fully represent the influence of dissolved material on the thermodynamic properties of seawater. We consider three distinct representations of salinity that have been used in previous studies and discuss the connections and distinctions between them. One of these variables provides the most accurate representation of density possible as well as improvements over Reference Salinity for the determination of other thermodynamic properties. It is referred to as "Density Salinity" and is represented by the symbol SAdens; it stands out as the most appropriate representation of salinity for use in dynamical physical oceanography. The other two salinity variables provide alternative measures of the mass fraction of dissolved material in seawater. "Solution Salinity", denoted SAsoln, is the most obvious extension of Reference Salinity to allow for composition anomalies; it provides a direct estimate of the mass fraction of dissolved material in solution. "Added-Mass Salinity", denoted SAadd, is motivated by a method used to report laboratory experiments; it represents the component of dissolved material added to Standard Seawater in terms of the mass of material before it enters solution. We also discuss a constructed conservative variable referred to as "Preformed Salinity", denoted S∗, which will be useful in process-oriented numerical modelling studies. Finally, a conceptual framework for the incorporation of composition anomalies in numerical models is presented that builds from studies in which composition anomalies are simply ignored up to studies in which the influences of composition anomalies are accounted for using the results of biogeochemical models. 1TEOS-10: international Thermodynamic Equation of Seawater 2010, http://www.teos-10.org/.

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)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.002
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.228
Teacher spread0.199 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations104
Published2011
Admission routes1
Has abstractyes

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