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Record W2558114395 · doi:10.3390/s16122027

A Multi-Pumping Flow System for In Situ Measurements of Dissolved Manganese in Aquatic Systems

2016· article· en· W2558114395 on OpenAlexaboutno aff
David L. Meyer, Ralf D. Prien, Olaf Dellwig, Joanna J Waniek, Ingo Schuffenhauer, Jan Donath, Siegfried Krüger, Malte Pallentin, Detlef E. Schulz‐Bull

Bibliographic record

VenueSensors · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsAbsorbanceManganeseDetection limitChemistryFlow injection analysisIn situDissolved organic carbonInductively coupled plasmaSeawaterAnalytical Chemistry (journal)SpectrophotometryEnvironmental chemistryEnvironmental scienceChromatographyGeologyPlasma

Abstract

fetched live from OpenAlex

A METals In Situ analyzer (METIS) has been used to determine dissolved manganese (II) concentrations in the subhalocline waters of the Gotland Deep (central Baltic Sea). High-resolution in situ measurements of total dissolved Mn were obtained in near real-time by spectrophotometry using 1-(2-pyridylazo)-2-naphthol (PAN). PAN is a complexing agent of dissolved Mn and forms a wine-red complex with a maximum absorbance at a wavelength of 562 nm. Results are presented together with ancillary temperature, salinity, and dissolved O 2 data. Lab calibration of the analyzer was performed in a pressure testing tank. A detection limit of 77 nM was obtained. For validation purposes, discrete water samples were taken by using a pump-CTD system. Dissolved Mn in these samples was determined by an independent laboratory based method (inductively coupled plasma-optical emission spectrometry, ICP-OES). Mn measurements from both METIS and ICP-OES analysis were in good agreement. The results showed that the in situ analysis of dissolved Mn is a powerful technique reducing dependencies on heavy and expensive equipment (pump-CTD system, ICP-OES) and is also cost and time effective.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score0.686

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 studies0.0000.000
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 head0.035
GPT teacher head0.229
Teacher spread0.193 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

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