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Record W4250646586 · doi:10.5194/os-2020-72

Norwegian Sea net community production estimated from O <sub>2</sub> and prototype CO <sub>2</sub> optode measurements on a Seaglider

2020· preprint· en· W4250646586 on OpenAlexaff
Luca Possenti, Ingunn Skjelvan, Dariia Atamanchuk, Anders Tengberg, Matthew Humphreys, Socratis Loucaides, Liam Fernand, Jan Kaiser

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsDalhousie University
FundersNatural Environment Research CouncilEngineering and Physical Sciences Research Council
KeywordsOptodeAlkalinityAnimal scienceEnvironmental scienceSalinityChemistryAnalytical Chemistry (journal)Environmental chemistryOceanographyGeologyIon

Abstract

fetched live from OpenAlex

Abstract. We report on a pilot study using a CO2 optode deployed on a Seaglider in the Norwegian Sea for 8 months (March to October 2014). The optode measurements required drift- and lag-correction, and in situ calibration using discrete water samples collected in the vicinity. We found the optode signal correlated better with the concentration of CO2, c(CO2), than with its partial pressure, p(CO2). Using the calibrated c(CO2) and a regional parameterisation of total alkalinity (AT) as a function of temperature and salinity, we calculated total dissolved inorganic carbon concentrations, CT, which had a standard deviation of 10 µmol kg−1 compared with direct CT measurements. The glider was also equipped with an oxygen (O2) optode. The O2 optode was drift-corrected and calibrated using a c(O2) climatology for deep samples (R2 = 0.89; RMSE = 0.009 µmol kg−1). The calibrated data enabled the calculation of CT – and oxygen-based net community production, N(CT) and N(O2). To derive N, CT and O2 inventory changes over time were combined with estimates of air-sea gas exchange and entrainment of deeper waters. Glider-based observations captured two periods of increased Chl a inventory in late spring (May) and a second one in summer (June). For the May period, we found N(CT) = (24±5) mmol m−2 d−1, N(O2) = (61±14) mmol m−2 d−1 and an (uncalibrated) Chl a peak concentration of craw(Chl a) = 3 mg m−3. During the June period, craw(Chl a) increased to a summer maximum of 4 mg m−3, which drove N(CT) to (64±67) mmol m−2 d−1 and N(O2) to (166±75) mmol m−2 d−1. The high-resolution dataset allowed for quantification of the changes in N before, during and after the periods of increased Chl a inventory. After the May period, the remineralisation of the material produced during the period of increased Chl a inventory decreased N(CT) to (−80±107) mmol m−2 d−1 and N(O2) to (−15±27) mmol m−2 d−1. The survey area was a source of O2 and a sink of CO2 for most of the summer. The deployment captured two different surface waters: the Norwegian Atlantic Current (NwAC) and the Norwegian Coastal Current (NCC). The NCC was characterised by lower c(O2) and CT than the NwAC, as well as lower N(O2), N(CT) and craw(Chl a). Our results show the potential of glider data to simultaneously capture time and depth-resolved variability in CT and O2.

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)0.000
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 score0.023
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.063
GPT teacher head0.252
Teacher spread0.189 · 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
Published2020
Admission routes1
Has abstractyes

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