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Record W7098939193

i Seasonal Variability of Groundwater Contribution to Watershed Discharge

2016· article· en· W7098939193 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicSocial Robot Interaction and HRI
Canadian institutionsnot available
Fundersnot available
KeywordsSnowmeltGroundwaterSpring (device)Hydrology (agriculture)PermafrostWatershedDissolved organic carbonGroundwater discharge
DOInot available

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.657
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0430.002

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.

Opus teacher head0.024
GPT teacher head0.336
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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