Rapid fingerprinting technology of heavy oil spill by mid-infrared spectroscopy
Bibliographic record
Abstract
With the increase of unconventional oil production and transportation, the detection methods of light crude oil have been challenged. Mid-Infrared spectroscopy can reflect the functional group of the oil related samples, which has strong absorption signals with distinguishable peaks featured as a fast, economy, and robust technique. Nevertheless, the previous study and application of oil relevant samples, such as petroleum chemical industry online monitoring, are mainly based on Near-infrared spectroscopy. Recently, the rapid development of the spectral instrument manufacturing and the data analysis methods provides a more comprehensive technical support for the rapid and accurate identification of marine oil spill by Mid-infrared spectroscopy. In this paper, 10 crude oil samples were selected for infrared spectroscopy detection, and the results were analysed and compared with those of gas chromatography flame ionization detection method. The character information of the IR spectra and GC/FID chromatograms were extracted and classified both by principal component analysis and partial least squares regression. Under the condition of small sample size, the recognition accuracy was up to 100%. The results show that the mid-infrared method combined with chemometrics can be expected to achieve rapid, accurate and economical identification of heavy oil species.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".