How can wireless, mobile data acquisition be used for taking part of the lab to the sample, and how can it join the internet of things?
Bibliographic record
Abstract
During the last several years, the world has moved from wired communications (e.g., a wired ethernet, wired telephone) to wireless communications (e.g., cell phones, smart phones, tablets). However, data acquisition has lagged behind and for the most part, data in laboratory settings are still acquired using wired communications (or even plug in boards). In this paper, approaches that can be used for wireless data acquisition are briefly discussed using a conceptual model of a future, mobile, portable micro-instrument as an example. In addition, past, present and near-future generations of communications are discussed; processors, operating systems and benchmarks are reviewed; networks that may be used for data acquisition in the field are examined; and, the possibility of connecting sensor or micro-instrument networks to the internet of things is postulated.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.004 | 0.001 |
| 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 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".