Viability of motes for hydrological measurement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Low‐cost, low‐power wireless sensor networks (mote networks) have the potential to revolutionize data collection methods in hydrology. They promise the ability to monitor catchments at very high spatial and temporal resolution with flexible sampling schemes, real time data processing and high levels of quality control. We operated an experimental network of 41 motes monitoring seven different parameters each at 15 min intervals for 10 months in a small forested catchment in southwestern British Columbia, Canada, to determine if this emerging technology is suitable for use by hydrologists in its current form. Our particular interests were ease of setup, sampling reliability, power consumption, and hardware resilience. We found that while motes gave the ability to monitor a catchment at resolution levels that were previously impossible, they still need to evolve into an easier to use, more reliable platform before they can replace traditional data collection methods.
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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 | 0.004 | 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 | 0.000 | 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) | 0.000 | 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 it