Evaluation of the hybrid Air2stream model for simulating daily stream temperature during extreme summer heat wave and autumn drought conditions - Data sets and scripts
Bibliographic record
Abstract
Overview This repository contains the data and scripts used in the analyses presented in the following publication: Callahan, L. and Moore, R.D. Evaluation of the hybrid Air2stream model for simulating daily stream temperature during extreme summer heat waves and autumn drought conditions. Hydrological Processes, DOI: 10.1002/hyp.70033. Data files a2s_8_parameter_values.csv This file contains the values of the calibrated parameters a1 to a8 for the 8-parameter version of air2stream for each of the 23 hydrometric stations used in the analysis. stn_hyd_reg.csv This file contains the following information for each hydrometric station used in the analysis: · Water Survey of Canada (WSC) station number (station_number) · hydrologic regime (regime) · Water Survey of Canada station name (station_name) · regulation status (status) ts_all_58.csv This file contains daily time series of the input data and simulated and observed water temperatures for each of the 23 hydrometric station. · station_number - WSC station number · year, month, day - date · ta - daily mean air temperature · tw_obs - daily mean observed water temperature (°C) · tw_sim_8 - daily mean water temperature simulated by the 8-parameter version of air2stream (°C) · q - mean daily streamflow (m3s-1) · period - calibration/validation · date - date in yyyy-mm-dd format · tw_sim_5 - daily mean water temperature simulated by the 5-parameter version of air2stream (°C) Scripts 01_generate_map.R Generates a map showing hydrometric station locations within British Columbia 01_cal_val_stats.R Computes root-mean-square error and mean bias error for calibration and validation periods for each hydrometric station. 01_generate_ts_plots.R Generates time-series plots of observed and simulated water temperatures. 02_compare_air2stream_empirical.R Computes RMSE and MBE for both versions of air2stream and five mixed-effects models.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".