Calibration of a modified temperature‐light intensity logger for quantifying water electrical conductivity
Bibliographic record
Abstract
Abstract The Stream Temperature, Intermittency, and Conductivity (STIC) electrical conductivity (EC) logger as presented by Chapin et al. (2014) serves as an inexpensive (∼50 USD) means to assess relative EC in freshwater environments. This communication demonstrates the calibration of the STIC logger for quantifying EC, and provides examples from a month long field deployment in the High Arctic. Calibration models followed multiple nonlinear regression and produced calibration curves with high coefficient of determination values (R2 = 0.995 − 0.998; n = 5). Percent error of mean predicted specific conductance at 25°C (SpC) to known SpC ranged in magnitude from −0.6% to 13% (mean = −1.4%), and mean absolute percent error (MAPE) ranged from 2.1% to 13% (mean = 5.3%). Across all tested loggers we found good accuracy and precision, with both error metrics increasing with increasing SpC values. During 10, month‐long field deployments, there were no logger failures and full data recovery was achieved. Point SpC measurements at the location of STIC loggers recorded via a more expensive commercial electrical conductivity logger followed similar trends to STIC SpC records, with 1:1.05 and 1:1.08 relationships between the STIC and commercial logger SpC values. These results demonstrate that STIC loggers calibrated to quantify EC are an economical means to increase the spatiotemporal resolution of water quality investigations.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".