Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The selective removal of nitrogen-containing compounds from oil and oil fractions is of interest because of the potential deleterious impact of such compounds on products and processes. Problems caused by nitrogen-containing compounds include gum formation, acid catalyst inhibition and deactivation, acid–base pair-related corrosion, and metal complexation. A brief overview of the classes of nitrogen compounds found in oil is provided. The review of processes to remove nitrogen from oil emphasizes studies that investigated denitrogenation of industrial feedstocks, such as refinery fractions, heavy oils, and bitumens. The main topics covered are hydrotreating, liquid–liquid phase partitioning, solvent deasphalting, adsorption, chemical conversion followed by separation, and microbial conversion. Chemical conversion processes include oxidative denitrogenation, N-alkylation, complexation with metal salts, and conversion in high-temperature water. There are many processes for denitrogenation by separation of the nitrogen-rich products from oil without removing the nitrogen group from the nitrogen-containing compounds. As a consequence, most of these processes are viable mainly for removal of nitrogen from low-nitrogen-content oils, typically with <0.1 wt % N. At present, hydrodenitrogenation appears to be the only industrially viable process for nitrogen removal from oils with high nitrogen content.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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) | 0.001 | 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 it