Kosñipata River discharge at San Pedro and Wayqecha, Peru (Clark et al. 2014)
Bibliographic record
Abstract
Please cite: Clark, K. E., Torres, M. A., West, A. J., Hilton, R. G., New, M., Horwath, A. B., Fisher, J. B., Rapp, J. M., Robles Caceres, A., and Malhi, Y. (2014), The hydrological regime of a forested tropical Andean catchment, Hydrology and Earth System Sciences, 18, 5377-5397, doi: 10.5194/hess-18-5377-2014. Sheet 1: Discharge measurements at the San Pedro gauging station (1360 m.a.s.l.), along the Kosñipata River, in the Andes of Peru. The Kosñipata River at the San Pedro gauging station drains an area of 164.4 km2. Field measurements consisted of river height, flow velocity, and cross-sectional area, which together allowed us to estimate discharge and runoff over the study period. River stage height was measured from January 2010 to February 2011 using a river logger (GlobalWater WL16 Data Logger, range 0–9 m), recording river level every 15 min. The instantaneous discharge associated with each height measurement was calculated based on calibrated stage–discharge relationships. The Kosñipata River discharge at San Pedro was measured through a complete water year, with a 31-day gap partly in July and August (during low flow) that was covered by three manual measurements and the gap was filled using linear interpolation. Sheet 2: Weekly to monthly discharge measurements at the Wayqecha gauging station (2250 m.a.s.l), along the Kosñipata River, in the Andes of Peru. The Wayqecha sub-catchment a nested catchment upstream of the San Pedro gauging station. It encompasses the headwaters of the Kosñipata River, draining an area of 48.5 km2 (See the supplementary information in Clark et al. 2014). Locations of the San Pedro and Wayqecha gauging stations are provided as GIS coverages in a companion dataset. This product was created by Kathryn Clark (kathryn.clark23@gmail.com). Other related datasets from Clark et al. (2014): Clark, K., J. West, R. Hilton (2017). Andes-Amazon gauging stations (Clark et al. 2014), HydroShare, http://www.hydroshare.org/resource/b541f44606a44a4a911e0e09d1b88d74 Clark, K., J. West, R. Hilton (2017). Kosñipata River at San Pedro, Peru (Clark et al. 2014), HydroShare, http://www.hydroshare.org/resource/b54b1cc138c54004a669f91a5351166e Clark, K., J. West, R. Hilton (2017). Catchment boundary, Kosñipata River at San Pedro, Peru (Clark et al. 2014), HydroShare, http://www.hydroshare.org/resource/0677a428cbd64d0ab62f7ab7a8e112f3 Clark, K., J. West, R. Hilton (2017). Catchment boundary, Kosñipata River at Wayqecha, Peru (Clark et al. 2014), HydroShare, http://www.hydroshare.org/resource/8a21d07106564bcdb2d183c77a5de877
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| 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) | 0.012 | 0.010 |
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".