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
Abstract A Fischer–Tropsch process always forms part of a larger indirect liquefaction facility, which consists of three processing steps. The first step is to convert a carbon source, such as coal, natural gas, biomass, or organic waste, into synthesis gas (syngas). Syngas is a mixture of hydrogen and carbon monoxide, and it is the feed material for a Fischer–Tropsch process, which is the second step in the indirect liquefaction process. Fischer–Tropsch synthesis is the catalytic polymerization and hydrogenation of CO, which produces a synthetic crude oil (syncrude). The syncrude is a multiphase mixture of hydrocarbons, oxygenates, and water. The third step is the refining of the syncrude to products that are traditionally produced from conventional crude oil, such as transportation fuels and petrochemicals. The current contribution deals only with the Fischer–Tropsch process; the generation of syngas and the refining of Fischer–Tropsch syncrude are not discussed in any detail. A Fischer–Tropsch process has three main elements: catalyst, reactor, and gas loop. Fischer–Tropsch catalysis is described to explain the relationship among the different catalyst types, operating conditions, and products. A description of the main syncrude types and their compositions is also provided. Fischer–Tropsch technologies are discussed, with an explanation of the relationship between catalyst and reactor, the tradeoffs involved in different catalyst–reactor combinations, as well as guidelines for technology selection. The role of the Fischer–Tropsch gas loop is outlined, with a discussion of the key elements of the gas loop and how they affect the overall performance of a Fischer–Tropsch process.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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