Development of a Multiple-Class High-Resolution Gas Chromatographic Relative Retention Time Model for Halogenated Environmental Contaminants
Bibliographic record
Abstract
A predictive model for the relative gas chromatographic retention times (GC-RRTs) of the following nine classes of halogenated environmental contaminants was developed: polybrominated diphenyl ethers (PBDEs); polychlorinated diphenyl ethers (PCDEs); polychlorinated biphenyls (PCBs); polychlorinated naphthalenes (PCNs); polychlorinated dibenzo-p-dioxins (PCDDs); polychlorinated dibenzofurans (PCDFs); polybrominated dibenzo-p-dioxins (PBDDs); polybrominated dibenzofurans (PBDFs); and organochlorine pesticides. MOPAC calculated physicochemical properties and structural descriptors in the model include molecular weight, square root of the number of halogen substituents, ionization potential, dipole moment, and the number of ortho, meta, and para halogen substituents. Using these variables, individual models for each of the contaminant classes were combined into a multiple class model incorporating the GC-RRTs of the 375 compounds of interest. The individual and multiclass GC-RRT models had acceptable fits between observed and predicted GC-RRTs (r2 = 0.9741-0.9990 for PBDEs, PCDEs, PCBs, PCNs, PCDD/Fs, and PBDD/Fs; r2 = 0.9250 for pesticides; and r2 = 0.9631 for the multiclass model) over a wide range of retention times and molecular structures. The combined model was tested on known GC-RRTs of hydroxylated PCBs and chlorinated phenoxyphenols and provided satisfactory results, demonstrating the strength of the model in predicting GC-RRT windows for contaminant classes not used in constructing the model. Such models will be useful in predicting the GC retention characteristics of novel environmental contaminants and their degradation products, for which analytical standards may not be available.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 0.001 |
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".