Bibliographic record
Abstract
Decarbonizing global energy systems is among the most pertinent challenges in the climate crisis. Tightly tied to the economic prosperity of a nation and responsible for more than 75% of global emissions, decarbonization of the energy sector will require well-developed solutions. The emergence of detonation engines can reduce emissions in aviation and gas-fired power generation, with theoretical efficiencies 40% lower than conventional engines. These efficiencies have yet to be realized in practice, due to our lack of understanding of detonation propagation and its instabilities. This project aimed to develop the laboratory systems required to study detonations. To this end, a long rectangular channel was manufactured and outfitted with a gas handling system to inject a mixture, an ignition system, pressure transducers to capture the propagation characteristics, and an optical system enabling Schlieren photography. The gas handling system uses sonic nozzles, thus flow rate is a linear function of upstream line pressure. Each line contains a pressure transducer, microcontroller to process signals, LCD for pressure readout, and a regulator for adjustment of the line pressure. The ignition system uses an ignition box that steps up a 12 V supply to 400 V, an ignition coil raising the voltage to 15-40 kV, and a spark plug to ignite the mixture. Ignition, pressure transducer signal acquisition, and Schlieren photography are all synchronized using a NI-6356 DAQ with LabVIEW. The DAQs analog input ports receive readings from the pressure transducers along the channel, the timing of which and known distance between transducers allows for velocity calculations. The schlieren system utilizes the digital output channels to synchronize camera shutter with an LED driver, and parabolic mirrors, positioned to optimize schlieren imaging. The LED driver requires greater current than produced by the DAQ thus a current amplifier circuit is used to bridge the DAQ and driver.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.055 | 0.033 |
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".