MétaCan
Menu
Back to cohort
Record W2101467182 · doi:10.5376/ijms.2015.05.0040

Distribution and Sources of Fatty Acids in Sediment Samples from Shatt Al-Arab Estuary and Northwest Arabian Gulf

2015· article· en· W2101467182 on OpenAlexvenueno aff
Al-Timari A.Ak, Al-Saad H.T, Douabul A.A, M.G. Salah, Hantoosh A.A

Bibliographic record

VenueInternational Journal of Marine Science · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsEstuaryOceanographySedimentDistribution (mathematics)Environmental scienceGeologyGeomorphology

Abstract

fetched live from OpenAlex

Surface sediments (10 cm) of the subtropical Shatt Al-Arab estuary and Northwest Arabian Gulf were collected to study the fatty acids distribution and sources .Samples were analyzed by gas chromatography. Other relevant parameters were measured including total organic carbon, and grain size. A total of 31 fatty acids were identified from the superficial sediment samples, including polyunsaturated PUFA, monounsaturated (MUFA), and saturated and branched fatty acids. The total FA (TFA) concentrations were in the range 13.32-35.72 µg/g dry weight (dw).Generally, the abundances of the 31 FAs showing strong even/odd numbered predominance, and ranging from C12 to C32 were C14 (1.68-12.45%), C16 (2.92-13.49%), C18 (0.87-12.75), C19 (0.87-3.29%), C24 (0.19-6.99%) and C26 (nd-8.90%), C28(nd-5.91%). These distributions are indicative of input of terrestrial, phytoplanktonic and bacterial lipid residues. The sums of long chain FAs (LCFAs) C24-C32 concentrations of were calculated. High values of terrestrial Organic Matter (OM)were found at Shatt Al-Arab, the values were in the range 4.05-4.46 µg/g (dw), with contribution relative to the TFAs ranging from 12.49-21.99% (mean 17.99%). While low values were found at the North West Arabian Gulf , the values were in the range 0.03-0.09 µg/g (dw), with contribution relative to the TFAs ranging from 0.19-0.68 % (mean 0.36%).

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)0.001
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 score0.062
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.016
GPT teacher head0.230
Teacher spread0.214 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Marine ScienceSame topicMarine and coastal ecosystemsFrench-language works237,207