An Analysis of the Thermal Regime and Energy Balance of a Subarctic Hydroelectric Reservoir Using Direct Measurements of Surface and Lateral Exchanges
Bibliographic record
Abstract
These data were used for the paper entitled "An Analysis of the Thermal Regime and Energy Balance of a Subarctic Hydroelectric Reservoir Using Direct Measurements of Surface and Lateral Exchanges " and submitted to Hydrological Processes on May 2023. Tables has the following variables : bernard.temp.2021.2022.csv : temp.degC = water temperature (°C) Debit_Romaine.xlsx ROMAI_3C = turbine flow Romaine 3 (m3/s) ROMAI_3B = spillway flow Romaine 3 (m3/s) ROMAI_2C = turbine flow Romaine 2 (m3/s) ROMAI_2B= spillway flow Romaine 2 (m3/s) Ro2_EddyPro.csv "Temps" : Time "T_air (°C)" : air temperature "q (kg/kg)" : specific humidity "Patmo (Pa)" : atmospheric pressure "e (Pa)" : pressure "es (Pa)" : pressure at saturation "Zeta (-)" : stability "WS (m/s)" : wind speed "H_gf (W/m2)" : sensible heat flux "LE_gf (W/m2)" : latent heat flux "R_net (W/m2)" : net radiation "Tw (°C)" : water temperature "Hs_70m (kJ/m2)" : heat storage Ro2_hypso.csv "Altitude above sea level (m)" "Area (m2)" = area at that altitude "Ro2 Depth (m)" = corresponding depth "normalized Area (%)" = normalized area at that depth "cumulated normalized Area (%)" = cumulative normalized area from bottom "cumulated normalized Area (%) from surface" = cumulative normalized area from surface Ro2_water_level.csv "RO2 Niveau (m)" = daily water level above mean sea level (m) Teau_gf_B1.csv : depth in meters Teau_gf_B2.csv : depth in meters Tw_Data_RO2RO3_20220816.xlsx Moyenne de Temp_Eau_Avg = turbine water temperature (°C)
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".