Bibliographic record
Abstract
This report is a complete and in-depth look into all of the work that went into building a motorized zoom controller for DSLR cameras. The key objectives of this project were to build a motorized zoom controller that would be much cheaper than any commercialized product of its type, while still retaining full functionality. As an additional objective, the design would allow for an autonomous control of zoom or focus during time lapse photography. This was done by iteratively designing, building,and debugging a mechanism from September 2012 to April 2013. Each unsuccessful design gave light to a new more functional one. Upon finally assembling a sufficiently functional design, we began shooting test footage of both time lapse and zoom control and further improve the design. By the end of the allotted project work time, there remain a few bugs. But the design has accomplished its main goals. We have created a prototype device that is capable of adapting to multiple lens geometries to somewhat smoothly control the zoom with adequate speed proportional to a rocker potentiometer. The very same device is capable of smoothly changing the focus of a camera lens while shooting time lapse photography, allowing for uncommon and eye-capturing time lapse footage. The smallest tested focus steps were about 0.21°, which was nearly the exact step size proposed in our project proposal (submitted Fall of 2012) as an adequately small step for smooth zoom or focus. This focus control is easily adaptable to different time lapse settings using a simple Arduino code. The project is left somewhat incomplete, with the main necessary additions being the elimination of jerkiness in the zoom motion, a glitch in the servo control code, mounting the electronics and power supply more portably, and some further time lapse control tests. The future of this device is bright with relatively easy implementation of wireless control and the possibility of swapping motors to a more suitable one.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 0.008 |
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".