Stage-Discharge Time Series - Calvert Island - Archived
Bibliographic record
Abstract
Streamflow calculation; a component of the Kwakshua Watersheds Program In natural streams it is not possible to continuously measure stream discharge, thus an indirect approach was used, where river height (stage) was continuously measured at a gauging station using a pressure transducer, with periodic manual measurements of discharge along the range of potential stages to develop a stage-discharge rating curve. Low flows were manually measured using the velocity-area method, with either a Swoffer Current Velocimeter or a Sontek Acoustic Doppler Velocimeter. Moderate to high flows (generally greater than 1cms) were measured using the salt dilution method, either manually (dry salt) and/or remotely (starting in the fall of 2015), using a fully automated system to release pre-defined volumes of salt solution at pre-defined water stages at an upstream location, with permanently installed electrical conductivity sensors located down-stream, one on either side of the stream to measure the salt wave passing through. Data are available in near real-time using the Hakai Telemetry Network (Floyd and Brunsting, 2015). A calibration factor, required for the salt dilution method, was manually calculated at a minimum twice per barrel refill of salt solution, once at the initial fill and the other with the remaining solution before re-fill. All discharge measurements were assigned a relative uncertainty, based on fluctuations in the flow velocity profile (for area-velocity method), or based on the uncertainty in the volume of salt solution, the EC sensor resolution and the EC sensor calibration factor (for salt dilution method). Measurements with uncertainties higher than 20%, with noise or malfunctioning conductivity sensors, or with high uncertainties in stage monitoring were excluded from further analysis. The remaining discharge-stage measurements were plotted as a power-law equation (Q = Ce*(H-h0)^A) in excel, to analyze if there were clear outliers, to determine the approximate value of h0 and to determine if the data could be fitted on one curve, or if they would fit better on a low flow and high flow curve, separated by an 'inflection point'. After this, the rating curve equation was optimized using a non-linear least-squares fitting Python model (LMFit, 2015). A detailed description of these methods have been documented in the MSc thesis of Maartje Korver (2015). Finally, this discharge time-series was created using 5 minute average stage measurements. Extra caution must be taken when using calculated discharges greater than the highest measured discharge (noted in this file as 'Max measured discharge' ), because the extrapolation of a rating curve beyond a set of measurements is usually highly uncertain and can greatly over or under estimate discharge. THESE DATA are provided AS IS and will continuously improve as additional discharge measurements are taken. Users should re-check for periodic updates to the rating curves and subsequent discharge files. If errors are found please contact Bill.Floyd@viu.ca.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".