Infraestructura tecnológica de vigilancia remota
Bibliographic record
Abstract
In this investigation was made an evaluation of the different technological infrastructures of remote surveillance, in order to determine which standard is more suitable to be applied in the development of this infrastructure whit these characteristics, plus are evaluated different manufacturer of this kind of solutions in order to get the best product, to guarantee robust, moving and safety, and other applications. The present investigation is defined as a feasible project, and whose design is no experiment, of the descriptive transeccional kind because it surveys the ocurrences and the variable's values that shows up. About the instrument used, this was a questionnaire that let collect all that necessary information to development of the proposal, based in the opinion of investigations sample. To conclude this investigation can establish that the update of this technological infrastructure in the Rafael Urdaneta Bridge is highly feasible, because its characteristic and its applications let do it and the standard that was chosen MPEG is the most suitable for the proposal applied, inclusive the evaluated products for the application this technological surveillance brings a lot of benefits in all of supervision areas of operations in the bridge and it will be a important tool in the shape process and supervise all of apply of the different zones so this would be first update in this kind of handling of surveillance in the present.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".