The HLD Study of Surfactant Partitioning for Oilfield Corrosion Inhibitors
Bibliographic record
Abstract
Abstract The partitioning of corrosion inhibitor (CI) products is a measure of their potential to protect oilfield pipelines. In this paper the hydrophilic–lipophilic deviation (HLD) model is first used to quantify their partitioning in terms of the characteristic curvature ( C c,act ) of a series of anionic (alkoxylated phosphate esters) and cationic (alkoxylated amines, aromatic amines, imidazoline acetates and quaternary amines) actives. This parameter is expressed over a range of pHs within which pipeline corrosion occurs. The HLD model is next used to predict the partitioning of each active from water into toluene at increased salinities. Linear mixing rules are lastly used to predict the characteristic curvature of Product A ( C c,mix ) as a function of the C c,act of a subset of actives.
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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 | 0.001 | 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) | 0.000 | 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".