Methods for Interpreting the Partitioning and Fate\nof Petroleum Hydrocarbons in a Sea Ice Environment
Bibliographic record
Abstract
Decreases\nin Arctic Sea ice extent and thickness have led to more\nopen ice conditions, encouraging both shipping traffic and oil exploration\nwithin the northern Arctic. As a result, the increased potential for\naccidental releases of crude oil or fuel into the Arctic environment\nthreatens the pristine marine environment, its ecosystem, and local\ninhabitants. Thus, there is a need to develop a better understanding\nof oil behavior in a sea ice environment on a microscopic level. Computational\nquantum chemistry was used to simulate the effects of evaporation,\ndissolution, and partitioning within sea ice. Vapor pressures, solubilities,\noctanol–water partition coefficients, and molecular volumes\nwere calculated using quantum chemistry and thermodynamics for pure\nliquid solutes (oil constituents) of interest. These calculations\nincorporated experimentally measured temperatures and salinities taken\nthroughout an oil-in-ice mesocosm experiment conducted at the University\nof Manitoba in 2017. Their potential for interpreting the relative\nmovements of oil constituents was assessed. Our results suggest that\nthe relative movement of oil constituents is influenced by differences\nin physical properties. Lighter molecules showed a greater tendency\nto be controlled by brine advection processes due to their greater\nsolubility. Molecules which are more hydrophobic were found to concentrate\nin areas of lower salt concentration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.041 | 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 teacher head, 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".