Exploration of PocketLab Devices for UNI’s Campus, Classrooms, and Community
Bibliographic record
Abstract
PocketLab devices by Myriad Sensors were acquired by UNI’s Earth and Environmental Science department in the fall of 2022 through the Carver Grant. These citizen science devices are incredibly useful in educating and exploring real life issues in the environmental sciences that we are experiencing today. Issues such as air quality and weather are increasingly important topics to examine as the climate warms. During the summer of 2023, Iowa has had multiple cases of unhealthy air quality levels due to particulate matter from fires in Canada. Southern states have experienced extreme heat indexes, pointing to the need to consider the effects local environments have on weather aspects such as temperature. The PocketLab devices are very useful in displaying data in real time that can be used to help educate the general population on these real-life issues. Using the PocketLab devices as a tool to guide their learning, I have created and conducted three camps this summer with middle school and high school students on the concepts of air quality and weather.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".