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Record W4214658250 · doi:10.1007/s11270-022-05557-1

Marine Snow-Oil Interaction Affects n-Alkane Biodegradation in Sediment

2022· article· en· W4214658250 on OpenAlexafffund
Shokouh Rahsepar, Justine S. van Eenennaam, Jagoš R. Radović, Thomas B. P. Oldenburg, H.H.M. Rijnaarts, Albertinka J. Murk, E.M. Foekema, Alette Langenhoff

Bibliographic record

VenueWater Air & Soil Pollution · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsUniversity of CalgaryCanada Foundation for InnovationGulf of Mexico Research Initiative
KeywordsMarine snowSnowBiodegradationEnvironmental scienceSedimentSedimentationSeabedOceanographySnow removalDeepwater horizonOil spillPetroleumGeologyEcologyEnvironmental engineeringGeomorphologyWater column

Abstract

fetched live from OpenAlex

Abstract During the Deepwater Horizon (DwH) oil spill, an excessive production of marine snow was observed, and it was estimated that as much as 14% of the oil was transferred to the ocean floor by MOSSFA (Marine Oil Snow Sedimentation and Flocculent Accumulation). MOSSFA is an important pathway of transferring oil to the ocean floor. We performed experiments at laboratory scale in 15 aquaria, representing 5 exposures of marine snow with or without oil, only oil, and controls with only clay or sediment. We developed a method to produce artificial marine snow, which resembles the natural marine snow. Results showed 40% less biodegradation of alkanes in “marine snow with oil” compared to “only oil.” Most probably, this is due to preferred biodegradation of marine snow organics comparing to oil alkanes. Biodegradation of marine snow reduces the dissolved oxygen concentration, which might result in anaerobic conditions in the sediment layer. This finding can be projected to a potential ocean floor effect.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.511
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.006
GPT teacher head0.201
Teacher spread0.195 · 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.

Study designBench or experimental
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

Citations6
Published2022
Admission routes2
Has abstractyes

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