Dataset for: Simulating a spill of diluted bitumen: Environmental weathering and submergence in a model freshwater system
Bibliographic record
Abstract
The main petroleum product transported through pipelines in Canada is diluted bitumen (dilbit), a semi-liquid form of heavy crude oil mixed with natural gas condensates to facilitate transport. The weathering, fate, behaviour, and environmental effects of dilbit are crucial to consider when responding to a spill, however few environmental studies on dilbit have been completed. Here we report on 11-day long experimental spills of dilbit (Cold Lake Winter Blend) in outdoor micro-cosms meant to simulate a low-energy aquatic system containing natural lake water and sedi-ments treated with a low (1:8,000 oil:water) and high (1:800 oil:water) volume of dilbit. In the first 24 hours of the experiment, volatile hydrocarbons quickly evaporated from the dilbit, result-ing in increased dilbit density and viscosity. These changes in dilbit’s physical and chemical properties ultimately led to its submergence after 8 days. We also detected rapid accumulation of polycyclic aromatic compounds in the water column of the treated-microcosms following the spills. Our study provides new information on the environmental fate and behaviour of dilbit in a freshwater environment that will be critical to environmental risk assessments of proposed pipe-line projects. In particular, our study demonstrates the propensity for dilbit to sink under ambient environmental conditions in fresh waters typical of many boreal lakes.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".