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Record W3209431077 · doi:10.1002/lol2.10218

A case for addressing the unresolved role of permeable shelf sediments in ocean denitrification

2021· article· en· W3209431077 on OpenAlexafffund
Emily J. Chua, Markus Huettel, Katja Fennel, Robinson W. Fulweiler

Bibliographic record

VenueLimnology and Oceanography Letters · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsDalhousie University
FundersNational Science Foundation of Sri LankaNatural Sciences and Engineering Research Council of CanadaRhode Island Sea Grant, University of Rhode IslandMIT Sea Grant, Massachusetts Institute of TechnologyOcean Frontier InstituteNational Science Foundation
KeywordsContinental shelfOceanographyNitrogen cycleEnvironmental scienceDenitrificationNitrogenProductivityEarth scienceGeologyChemistry

Abstract

fetched live from OpenAlex

Abstract The marine nitrogen cycle is a main driver of ocean productivity and affects global climate. Despite decades of study, we still have an incomplete understanding of the role of the marine nitrogen cycle in the Earth system. While marine sediments play a major role in nitrogen cycling in the ocean, magnitudes and mechanisms are largely unconstrained. Recent research suggests that permeable sandy sediments on the highly energetic and dynamic continental shelf are key components of the marine nitrogen cycle, but data to quantify their contribution are lacking. Here, we use insights from measurements and modeling studies to substantiate the hypothesis that shelf sediments are an overlooked driver of the marine nitrogen cycle. Specifically, we propose that permeable shelf sediments are sites for intense nitrogen conversions and suggest a three‐pronged approach to address unresolved controversies.

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 score0.094
Threshold uncertainty score0.241

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 head0.021
GPT teacher head0.215
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

Citations23
Published2021
Admission routes2
Has abstractyes

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