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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".