Bibliographic record
Abstract
Abstract Heavy oil upgrading experiments were conducted using a variable frequency microwave. While microwave energy itself is readily available as a laboratory technique, microwave ovens that produce frequencies other than 2450 MHz are not. We were able to determine the effect of frequency, by using a variable fre- quency unit at a facility in North Carolina. Results were promising despite the rapid screening which was employed. Experimental variables included additives, reaction time, and frequency. The addition of activated carbon produced an oil that met pipeline specifications for viscosity and density. Coke formation determined only on the molybdic acid–iron powder combination; was less than 2 wt.%. This encouraging coke finding may indicate a low coking propensity due to selective heating whereby the bulk of the sample remains at cooler temperatures and reactions are carried out very rapidly. Overall, the experimental results emphasized that different combinations of material interacted differently at different frequencies. This finding is of utmost importance because the screening of additives has traditionally been focused on 2450 MHz and 915 MHz, which — depending upon the reactants — may not be appropriate for optimum interaction. Frequency was found to have a marked effect upon upgrading.
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.
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 | 0.000 | 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) | 0.001 | 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".