Reply to the Discussion by S. Rayne of “A water resources management strategy for small water districts — a case study of the South East Kelowna irrigation district”
Bibliographic record
Abstract
Unless otherwise stated, the temperature and precipitation data used in this research came from the Canadian weather office (http://www.climate.weatheroffice.gc.ca). The use of two weather stations was required as a complete set of data was not available from 2005 through to 2010 from a single metering location. From 2005 to 2008 the data came from the Kelowna AWOS station (http:// climate.weather.gc.ca/climateData/dailydata_e.html?timeframe= 2&Prov=&StationID=30954&cmdB1=Go&Year=2005&Month=5& cmdB1=Go#)whichwas located at 49°57=22.000==N, 119°22=40.000==W. The year 2009 was not included in the analysis as this was a year with abnormally low inflows into the reservoir and the South East Kelowna Irrigation District imposed restrictions on water use. For 2010 the data came from the Kelowna station (http://climate. weather.gc.ca/climateData/dailydata_e.html?timeframe=2&Prov= BC&StationID=48369&Year=2010&Month=5&cmdB1=Go#) located at 49°57=26.000==N, 119°22=40.000==W. Both stations are located at the Kelowna airport and are approximately 120 m apart. This study covered the months of May to September as these were the months found to have significant water demand. Temperature or precipitation data was found to be missing in less than one percent of the total data (six out of a total of 765 days). For the days with missing data, an average was taken of the day before and the day after to arrive at a value for the day.
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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 | 0.000 | 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 | 0.000 | 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".