Fate of groundwater inflow in Lake Thingvallavatn during early spring ice-breakup
Bibliographic record
Abstract
Sub-artic Lake Thingvallavatn is one of Iceland´s largest, deepest and best known lakes. Situated at the rift between the North American and Eurasian tectonic plates, it is part of a world heritage site and a major tourist destination. From a hydrological viewpoint, the lake is unique in that it is predominantly fed by groundwater springs originating from nearby glacier Langjokull. The goal of this study was to establish the near field inflow dynamics of the largest subsurface spring Silfra, contributing approximately 30% of the total inflows to the lake, during early spring ice-breakup. A ten day field study was conducted in February 2009. The groundwater inflows were found to have higher temperature, conductivity, and pH than the receiving lake water. Using temperature as a tracer, the groundwater fate, and mixing regimes were assessed both in open water and under ice, as ice was breaking up and shifting in and out of the study area during the study period. Initial results from moored thermistor chains, CTD profiles, ADV measurements, weather stations and Autonomous Underwater Vehicle (AUV) borne CTD will shed a stronger light on the interaction of river inflows, ice cover and meteorological forcings during winter ice cover and early spring break-up. The use of an AUV platform to collect horizontal CTD profiles characterizes horizontal variability of water properties in open and ice-covered water, something that cannot be obtained using conventional techniques.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".