Bibliographic record
Abstract
Abstract Sensors to measure gas velocities in high temperature flows need to be robust, low-profile so that they do not obstruct the flow, and easy to apply on metal surfaces. Thermal spray offers a method of making low-cost sensors that can be applied on large areas. Plasma spray was used to deposit an electrically insulating layer of alumina on a 316 stainless steel block. A 17 mm diameter heater coil was deposited on top of the alumina layer by spraying Nichrome from a twin wire arc spray system through a 3D printed polymer mask. A thermocouple junction was built next to the heater by inserting an insulated Constantan wire through a vertical hole drilled in the steel block and spraying steel on the top of the hole to close it and form an electrical connection between the wire and the surrounding substrate. The junction of the wire and the steel formed a thermocouple whose output voltage was calibrated. A flow loop was built to calibrate the sensor by passing air over it at velocities of up to 5 m/s. A series of 2 min long voltage pulses were applied to the heater, increasing its temperature by approximately 5°-10°C each time, before letting it cool. A calibration curve was developed of the air velocity as a function of the time constant for cooling of the sensor.
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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".