Method development for aquatic ecotoxicological characterization factor calculation for hydrocarbon mixtures in life cycle assessment
Bibliographic record
Abstract
Most pollutants are released into the environment in the presence of other contaminants, creating complex mixtures. In life-cycle impact assessment (LCIA) methods, characterization factors (CFs) are used to obtain the potential impacts associated with each contaminant emission. Current LCIA methods do not include CFs to evaluate the potential impacts of complex organic mixtures on ecosystems. This study explores the possibility of developing new CFs for petroleum mixtures. Petroleum products are an example of mixtures whose constituents have a common toxic mode of action: the narcosis effect. Characterization factors were calculated for a series of representative constituents of a specific petroleum mixture and also for different fractions of the same mixture developed using the hydrocarbon block (HBM) and Total Petroleum Hydrocarbon Criteria Working Group (TPHWG) methods. Finally, CFs were developed for the mixture itself as a whole by using experimental property measurements and estimations. The soil-water partitioning coefficient, water solubility, degradation kinetic constant in soil, octanol-water partitioning coefficient, and vapor pressure were measured while the molar weight and the degradation kinetic constants in air, water, and sediments were estimated. The highest aquatic ecotoxicological CFs, no matter the approach chosen, were obtained for an emission to freshwater up to 2.2 × 10(+07) PAF·m(3) ·d/kg for the highest CF. CF distributions obtained using the different blocking method and experimental CFs obtained for oil as a whole are, on average, not significantly different, given the known uncertainty of ecotoxicological models in LCIA. Consequently, all the CFs obtained using the different blocking methods from the literature are considered relevant for characterizing the potential impact for aquatic ecotoxicity of petroleum substances.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".