A multi-parameter hydrochemical characterization of proglacial runoff, Cordillera Blanca, Peru
Bibliographic record
Abstract
Abstract. The Cordillera Blanca, located in the central Peruvian Andes, is the most glacierized mountain range in the tropics. The study objective is to determine the spatial and topographic controls on geochemical and isotopic parameters in the Quilcayhuanca drainage basin. During the dry season of July 2009, surface water and groundwater samples were collected from the proglacial zone of the 90 km2 Quilcayhuanca basin which is 20% glacierized. The basin water samples (n = 25) were analyzed for pH, conductivity, major cations (Ca, Mg, Na, K, Fe(II)), major anions (F, Cl, SO4), nutrients (total N, total P, and Si), and stable isotopes of water (δ18O, δ2H). The valley's surface water is acidic (pH 3–4) and is dominated by Ca2+, Mg2+, and SO42−, the last of which is likely due to pyrite oxidation. Total P and total N show no trend with elevation down valley, while Si generally increases with decreasing elevation. Groundwater samples are differentiated from surface water samples by lower pH, specific conductance, and total P and higher Na+, K+, HCO3−, Si, and δ18O. A two-component mixing model indicates that discharge from the watershed is approximately two-thirds surface water (mostly glacier melt) and one-third groundwater. The results were compared to data from the Rio Santa and indicate that this trend may persist at the regional scale.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".