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Record W2191205336 · doi:10.3133/ds967

Hydrologic data for the Walker River Basin, Nevada and California, water years 2010–14

2015· article· en· W2191205336 on OpenAlexfundno aff
Michael T. Pavelko, Erin L. Orozco

Bibliographic record

VenueData series · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Resources Studies
Canadian institutionsnot available
FundersBanting and Best Diabetes Centre, University of TorontoDeutscher Akademischer Austauschdienst
KeywordsHydrology (agriculture)Geological surveyGroundwaterSurface waterEnvironmental scienceSTREAMSStructural basinWatershedWater qualitySpring (device)GeologyEcologyEnvironmental engineering

Abstract

fetched live from OpenAlex

Walker Lake is a threatened and federally protected desert terminal lake in western Nevada. To help protect the desert terminal lake and the surrounding watershed, the Bureau of Reclamation and U.S. Geological Survey have been studying the hydrology of the Walker River Basin in Nevada and California since 2004. Hydrologic data collected for this study during water years 2010 through 2014 included groundwater levels, surface-water discharge, water chemistry, and meteorological data. Groundwater levels were measured in wells, and surface-water discharge was measured in streams, canals, and ditches. Water samples for chemical analyses were collected from wells, streams, springs, and Walker Lake. Chemical analyses included determining physical properties; the concentrations of major ions, nutrients, trace metals, dissolved gases, and radionuclides; and ratios of the stable isotopes of hydrogen and oxygen. Walker Lake water properties and meteorological parameters were monitored from a floating platform on the lake. Data collection methods followed established U.S. Geological Survey guidelines, and all data are stored in the National Water Information System database. All of the data are presented in this report and accessible on the internet, except multiple-depth Walker Lake water-chemistry data, which are available only in this report.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.004
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.161
GPT teacher head0.264
Teacher spread0.103 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2015
Admission routes1
Has abstractyes

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