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Record W4380264586 · doi:10.3389/fmars.2023.1134851

Autonomous observations of biogenic N2 in the Eastern Tropical North Pacific using profiling floats equipped with gas tension devices

2023· article· en· W4380264586 on OpenAlexaff
Craig McNeil, Eric A. D’Asaro, Mark A. Altabet, Roberta C. Hamme, Emilio García‐Robledo

Bibliographic record

VenueFrontiers in Marine Science · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversity of Victoria
FundersUniversity of Maryland Center for Environmental ScienceWoods Hole Oceanographic InstitutionNational Science Foundation
KeywordsOceanographyArgoNutrientEnvironmental scienceNitrateUpwellingNitrogenSalinityDenitrificationDeep seaEnvironmental chemistryChemistryGeology

Abstract

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Oxygen Deficient Zones (ODZs) of the world’s oceans represent a relatively small fraction of the ocean by volume (<0.05% for suboxic and<5% for hypoxic) yet are receiving increased attention by experimentalists and modelers due to their importance in ocean nutrient cycling and predicted susceptibility to expansion and/or contraction forced by global warming. Conventional methods to study these biogeochemically important regions of the ocean have relied on well-developed but still relatively high cost and labor-intensive shipboard methods that include mass-spectrometric analysis of nitrogen-to-argon ratios (N2/Ar) and nutrient stoichiometry (relative abundance of nitrate, nitrite, and phosphate). Experimental studies of denitrification rates and processes typically involve either in-situ or in-vitro incubations using isotopically labeled nutrients. Over the last several years we have been developing a Gas Tension Device (GTD) to study ODZ denitrification including deployment in the largest ODZ, the Eastern Tropical North Pacific (ETNP). The GTD measures total dissolved gas pressure from which dissolved N2 concentration is calculated. Data from two cruises passing through the core of the ETNP near 17 °N in late 2020 and 2021 are presented, with additional comparisons at 12 °N for GTDs mounted on a rosette/CTD as well as modified profiling Argo-style floats. Gas tension was measured on the float with an accuracy of< 0.1% and relatively low precision (< 0.12%) when shallow (P< 200 dbar) and high precision (< 0.03%) when deep (P > 300 dbar). We discriminate biologically produced N2 (ie., denitrification) from N2 in excess of saturation due to physical processes (e.g., mixing) using a new tracer – ‘preformed excess-N2’. We used inert dissolved argon (Ar) to help test the assumption that preformed excess-N2 is indeed conservative. We used the shipboard measurements to quantify preformed excess-N2 by cross-calibrating the gas tension method to the nutrient-deficit method. At 17 °N preformed excess-N2 decreased from approximately 28 to 12 µmol/kg over σ0 = 24–27 kg/m3 with a resulting precision of ±1 µmol N2/kg; at 12 °N values were similar except in the potential density range of 25.7< σ0< 26.3 where they were lower by 1 µmol N2/kg due likely to being composed of different source waters. We then applied these results to gas tension and O2 (< 3 µmol O2/kg) profiles measured by the nearby float to obtain the first autonomous biogenic N2 profile in the open ocean with an RMSE of ± 0.78 µM N2, or ± 19%. We also assessed the potential of the method to measure denitrification rates directly from the accumulation of biogenic N2 during the float drifts between profiling. The results suggest biogenic N2 rates of ±20 nM N2/day could be detected over >16 days (positive rates would indicate denitrification processes whereas negative rates would indicate predominantly dilution by mixing). These new observations demonstrate the potential of the gas tension method to determine biogenic N2 accurately and precisely in future studies of ODZs.

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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 score0.036
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.221
Teacher spread0.190 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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