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Record W2081272613 · doi:10.1071/mf13077

Warm-phase ENSO events modulate the continental freshwater input and the trophic state of sediments in a large South American estuary

2013· article· en· W2081272613 on OpenAlexfundno aff
Felipe García‐Rodríguez, Ernesto Brugnoli, Pablo Muñiz, Natalia Venturini, Letícia Burone, M. Hutton, Matilde Rodríguez, Ana Laura Pita, Noelia Kandratavicius, Laura Pérez, José E. Verocai

Bibliographic record

VenueMarine and Freshwater Research · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsnot available
FundersCanadian Institute of Steel ConstructionAgencia Nacional de Investigación e InnovaciónInstituto Nacional de Investigación y Tecnología Agraria y Alimentaria
KeywordsEstuaryTrophic levelEutrophicationOceanographySalinityPlanktonEl Niño Southern OscillationContinental shelfOrganic matterEnvironmental scienceBiogeochemistryLa NiñaGeologyEcologyClimatologyBiologyNutrient

Abstract

fetched live from OpenAlex

We studied the changes in geochemical variables in the middle section of Río de la Plata estuary during the 2009–2010 El Niño–Southern Oscillation (ENSO) event. Protein, organic matter, chlorophyll-a and phaeopigment content of surface sediments were significantly correlated with the increased continental freshwater input associated with high total monthly rainfall. During the warm-phase ENSO event, river flow was 5-fold larger than average historical levels, which led to a steady decrease in salinity values and the highest levels of geochemical variables. The evidence presented herein suggests that warm-phase ENSO events increase the trophic state of the sediments because of the increased freshwater input. Thus, our findings may be useful to anticipate potential eutrophication episodes in the study area.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.017
GPT teacher head0.259
Teacher spread0.242 · 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 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

Citations47
Published2013
Admission routes1
Has abstractyes

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