Robotic reconnaissance platform. I. Spectroscopic instruments with rangefinders
Bibliographic record
Abstract
In this paper, basic principles of the design and implementation of a portable, multi-functional scientific instrument, operating from a robotic reconnaissance mobile platform are discussed. The current version of the instrument includes a multi-gas laser sensor, multi-functional spectrometer, isotopes identifier, cameras, and rangefinder. An additional set of sensors monitors temperature, pressure, humidity, and background radiation. All components are installed on a mini-robotic platform, which provides data acquisition, processing, and transmittance. The design focuses on the development of calibration-free, reliable, low power-consumption devices. To create a highly survivable, accurate, and reliable instrument, a concept of an inhomogeneous sensory network has been developed. Such a network combines non-identical sensors and provides cross-use of information received from different sensors to describe environmental conditions, to choose appropriate algorithms of data processing, and to achieve high accuracy gas-concentration measurements. The system uses the same lasers to operate different optical devices such as sensors, rangefinders, spectrometers, and isotopes identifiers. Among the innovative elements described in this paper, are a calibration-free, laser multi-gas sensor with range-finding option; a high signal/noise ratio transmittance spectrometer; a single-frequency laser with nano-selector; and low repetition-rate femtosecond fiber lasers operating in near- and middle- infrared spectral ranges. New detailed analyses of absorption spectroscopy theoretical approximations made it possible to achieve high-accuracy gas-concentration measurements with miniature optical sensors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".