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Record W6924979706 · doi:10.1594/pangaea.912944

Diel oxygen production and uptake by Posidonia oceanica meadows at Elba, Italy

2020· dataset· en· W6924979706 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2020
Typedataset
Languageen
FieldBusiness, Management and Accounting
TopicEducation Systems and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsEddy covarianceSeagrassOxygenFlux (metallurgy)Diel vertical migrationVolume (thermodynamics)Turbulence

Abstract

fetched live from OpenAlex

Aquatic eddy covariance oxygen flux was determined over two seagrass (Posidonia oceanica) meadows at Elba, Italy. The first meadow (open-water) was located 300 m from the southwest corner of the island, and was studied over two continuous days from 15 to 18 May 2016. The second meadow (nearshore) was located 60 m from the north shore of the island, and was studied over two discontinuous days on 13 and 25 May 2017. Both meadows were located at 13 m depth. Eddy covaraince instruments were mounted to a lightweight frame and positioned over seagrass meadows such that the measurement volume was approximately 0.3 m above the top of the canopy. Eddy covariance velocity data were collected at 16 Hz with an acoustic Doppler velocimeter (Vector, Nortek-AS, Norway). Measurements of turbulent fluctuations in oxygen concentration were made 2 cm outside the measuring volume of the Vector using an optode minisensor (O2 Minisensor, Pyroscience GmbH, Germany). The 90% response time of the minisensor was less than 0.3 s. Stable oxygen measurements above and within the canopy were determined with galvanic oxygen sensors (OxyGuard, RBR Ltd., Canada). Eddy covariance fluxes were calculated from the product of turbulent fluctuations in vertical water velocity and oxygen concentration according to standard techniques (see Berg et al., 2003 for details on the aquatic eddy covariance technique, doi:10.3354/meps261075). Further details on calculations of flux, and their correction for the nighttime depletion of oxygen within the seagrass canopy, are presented in the linked manuscript (Koopmans et al., 2020, doi:10.3389/fmars.2020.00118).

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0390.017

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 head0.034
GPT teacher head0.240
Teacher spread0.205 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations1
Published2020
Admission routes1
Has abstractyes

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