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Record W3202624443 · doi:10.1002/lom3.10460

A mass spectrometer‐based pore‐water sampling system for sandy sediments

2021· article· en· W3202624443 on OpenAlexfundno aff
Emily J. Chua, Robert Short, Andres M. Cardenas‐Valencia, William B. Savidge, Robinson W. Fulweiler

Bibliographic record

VenueLimnology and Oceanography Methods · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsnot available
FundersNational Science Foundation of Sri LankaNatural Sciences and Engineering Research Council of CanadaFulbright CanadaBoston University
KeywordsBiogeochemical cycleSampling (signal processing)Environmental scienceDiel vertical migrationPore water pressureSedimentGeologyHydrology (agriculture)OceanographySoil scienceEnvironmental chemistryGeomorphologyChemistryGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract Highly permeable sandy sediments dominate the productive continental shelves and likely play a key role in global biogeochemical cycles. Despite their prominence, we currently have a poor understanding of how sandy sediments function and how they will respond to climate change. This knowledge gap is largely due to the difficulty in accurately sampling sandy sediments, as no method yet exists for measuring a range of analytes while accounting for advective pore‐water flow in these dynamic environments. To help address this, we developed a new pore‐water sampler that, when coupled to a portable mass spectrometer, can measure a suite of dissolved gases (e.g., O 2 , N 2 , Ar, CO 2 , and CH 4 ) in sandy sediments. Here, we present a series of laboratory experiments to validate and calibrate the instrument, as well as proof‐of‐concept submersion and field tests. Our results show that with some design improvements, our system will be capable of sustained (hourly to diel) in situ measurements in sandy sediments. This new approach has the potential to provide a large volume of high‐quality data in any aquatic system with sandy sediments and thus greatly advance our understanding of biogeochemical processes occurring in these environments.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.589
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.030
GPT teacher head0.286
Teacher spread0.255 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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