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
A compressed air sprayer was used to spray model paint onto two glass substrates at the same time. Afterwards, one glass substrate was placed on a LED light source and still photographs were taken from the top using a DSLR camera with a timer system. The other substrate was put on a balance to record weight. Pictures and weight measurements were taken at 5 second intervals for 15 minutes. The sprayed film thickness was varied. The pictures were analyzed using ImageJ software. Bubble Count vs. Time, Sauter Mean Diameter (SMD) of Bubbles vs. Time as well as Weight vs. Time was plotted. It was seen that the pace of weight loss was faster for thinner films. The rate of bubble escape also depended on film thickness. It took a longer time for thicker films to lose the bubbles entrapped in them. In the first 30 seconds, large bubbles escaped due to buoyancy forces and afterwards surface-tension driven flows became dominant. There was also a lot of bubble movement in thicker films. The effect of gravity was studied as well. Gravity did not affect the bubble escape rate since a downward facing film had the same bubble count as an upward facing film confirming that bubble motion was not due to buoyancy forces alone. However, the SMD of bubbles in a downward facing film was larger than an upward facing film. Buoyancy is not a factor in bubble escape from the downward facing film and only surface-tension driven flows play a role.
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) | 0.000 | 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) | 0.000 | 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 it