Crude oil and particulate fluxes including marine oil snow sedimentation and flocculant accumulation: Deepwater Horizon oil spill study
Bibliographic record
Abstract
Abstract The Deepwater Horizon oil spill is the largest in US history in terms of oil released and the amount of dispersants applied. It is also the first spill in which the incorporation of oil and/or dispersant into marine snow was directly observable. Marine snow formation, incorporation of oil (MOS – marine oil snow) and subsequent settling to the seafloor, has been termed MOSSFA: Marine Oil Snow Sedimentation and Flocculent Accumulation. This pathway accounts for a significant fraction of the total oil returning back to the sea floor. GOMRI funded studies have determined that important drivers of MOSSFA include, but are not limited to, an elevated and extended Mississippi River discharge, which enhanced phytoplankton production and suspended particle concentrations, zooplankton grazing, and enhanced mucus formation (operationally defined as EPS, TEP, marine snow). Efforts thus far to understand the mechanisms driving these processes are being used to aid in the development of response strategies. These include modeling efforts towards predicting plume dynamics. Although much has been learned during the GOMRI program (reviewed herein and elsewhere), there are still important unknowns that need to be addressed. Understanding of the conditions under which significant MOSSFA events occur, the consequences to the biology, the sinking velocity and distribution of the MOSSFA as well as its ultimate fate are amongst the most important consideration for future studies. Also important is the modification of the oil and dispersant within the MOS and its transport as part of MOSSFA. Ongoing studies are needed to further develop our understanding of these complex and interrelated phenomena.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".