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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".