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Record W1506217675 · doi:10.1002/grl.50275

Deep‐sea nutrient loss inferred from the marine dissolved N<sub>2</sub>/Ar ratio

2013· article· en· W1506217675 on OpenAlexaff
Roberta C. Hamme, Steven Emerson

Bibliographic record

VenueGeophysical Research Letters · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsAbyssal zoneBenthic zoneOceanographyDenitrificationSubarctic climateEnvironmental scienceDeep seaGeologyWater massNutrientNitrogenEcologyChemistry

Abstract

fetched live from OpenAlex

Abstract Some estimates of the budget of bioavailable nitrogen (fixed‐N) suggest that the oceanic nitrogen cycle is grossly out of balance. We use observations of dissolved N2/Ar ratios along abyssal flow paths in the ocean to investigate fixed‐N loss by benthic denitrification, one of the largest uncertainties. Dissolved N2/Ar in the deep‐sea increases from the North Atlantic to the North Pacific. At depths >3500 m, this can be explained by the mixing of low N2/Ar source waters from the North Atlantic with high N2/Ar source waters from the Southern Ocean. Benthic denitrification in sediments bathed by these abyssal waters is below the detection limit. However, measureable increases in N2/Ar at depths of 2000–3000 m between the subtropical and subarctic North Pacific, regions that share the same source water, must be caused by benthic denitrification. The Cascadia Basin, with high denitrification rates and connection to the open North Pacific, is a likely source.

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)0.001
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 score0.025
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.018
GPT teacher head0.235
Teacher spread0.217 · 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

Citations19
Published2013
Admission routes1
Has abstractyes

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