i Seasonal Variability of Groundwater Contribution to Watershed Discharge
Bibliographic record
Abstract
ii The objectives of this thesis were: (1) to quantify seasonal groundwater contribution to total stream discharge and (2) further our understanding of sub-arctic carbon sources and pathways within a sub-arctic discontinuous permafrost river catchment. Twenty-two samples were taken from the North Klondike River, 14 samples from 5 of its tributaries, and 46 rain and snowmelt samples from the Dawson City Airport, Yukon, Canada,. During the winter months, groundwater is responsible for greater than 95 % of total river discharge. Spring freshet and summer flow bring snowmelt and precipitation, contributing anywhere from 30 % to greater than 60 % of total river discharge. Groundwater is characterised by high concentrations of geogenic solutes from weathering during recharge, dissolved inorganic carbon, and carbon-14 activities of 0.61 pMC. Tritium activities indicate a fast moving system, with groundwater ages measuring less than 10 years. The most significant discharge of organic carbon from the system is during spring freshet (434,192 kg carbon). Primary productivity within the system is estimated to be 10.2 grams of carbon per metre squared, with approximately 96 % of carbon being sequestered or emitted as carbon dioxide. iii Acknowledgements I express my warm thanks to my supervisor Dr Ian Clark for his support, guidance, funding, and the opportunity to complete such a wonderful project. I would like to thank the staff in
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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) | 0.000 | 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".