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Record W1987783947 · doi:10.3989/scimar.2010.74s1011

Anthropogenic CO2 in the Azores region

2010· article· en· W1987783947 on OpenAlexaboutno aff
Fı́z F. Pérez, Javier Arı́stegui, M. Vázquez‐Rodríguez, Aida F. Rı́os

Bibliographic record

VenueScientia Marina · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsnot available
FundersConsejo Superior de Investigaciones CientíficasXunta de GaliciaMinisterio de Economía y CompetitividadEuropean Commission
KeywordsEnvironmental science

Abstract

fetched live from OpenAlex

The AZORES-I cruise was conducted in August 1998, spanning the length of three latitudinal large-scale sections at 22, 28 and 32ºW. The oceanic carbon system was oversampled by measuring total alkalinity, total inorganic carbon and pH. It is thus possible to estimate anthropogenic CO2 (CANT) and to investigate its relationship with the main water masses that are present. C CANT is calculated using the latest back-calculation techniques: φCTº and TrOCA methods. Although the two approaches produce similar vertical distributions, the results of the TrOCA method show higher CANT variability and produce higher inventories than those of the φCTº method. The large proportion of Mediterranean Water found in the northern part of the study area is the main cause of the observed increase northwards of C CANT inventories. Changes in C CANT inventories between 1981 and 2004 are evaluated using data from the TTO-NAS, OACES-93 and METEOR-60/5 cruises. According to the φCTº and TrOCA approaches, the average long-term rates of C CANT inventory change are 1.32±0.11 mol C m-2 y-1 (P=0.008) and 1.18±0.16 mol C m-2 y-1 (P=0.018), respectively. During the 1993-1998 a significant increase in the C CANT storage rate was detected by the φCTº method. It is thought that this stems directly from the enhanced Labrador Seawater formation after the increased advection observed at the time.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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.

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

Citations7
Published2010
Admission routes1
Has abstractyes

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