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Record W2137454783 · doi:10.4319/lo.2014.59.6.2150

Stable isotope estimates of evaporation : inflow and water residence time for lakes across the United States as a tool for national lake water quality assessments

2014· article· en· W2137454783 on OpenAlexaff
J. Renée Brooks, J. J. Gibson, S. J. Birks, Marc H. Weber, Kent D. Rodecap, John L. Stoddard

Bibliographic record

VenueLimnology and Oceanography · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsAlberta Innovates
FundersU.S. Environmental Protection Agency
KeywordsHydrology (agriculture)Environmental scienceWater qualityInflowPopulationWatershedPrecipitationSurface waterDrainage basinEcologyGeographyGeologyOceanography

Abstract

fetched live from OpenAlex

We used water δ 2 H and δ 18 O from ca. 1000 lakes sampled in the 2007 U.S. Environmental Protection Agency's National Lakes Assessment (NLA) to assess two hydrological variables—evaporation as a percentage of inflow ( E : I ) and water residence time ( τ ) for summer 2007. Using a population survey design, sampled lakes were distributed across the conterminous U.S., and results were scaled to the inference population (∼50,000 U.S. lakes). These hydrologic variables were related to lake nutrients and biological condition to illustrate their usefulness in national water quality monitoring efforts. For 50% of lakes, evaporation was < 25% of inflow, with values ranging up to 113% during the 2007 summer. Residence time was < 0.52 yr for half of the lakes and < 1.12 yr for 75% of lakes. Categorizing lakes by flow regime, 66.1% of lakes were flow‐though lakes (60% or more of the water flows through the lake, E : I < 0.4), 33.6% were restricted‐basin lakes (40% or more of the lake inflow evaporates, 0.4 < E : I < 1), and < 0.3% were closed basin (all water entering the lake leaves through evaporation, E : I > 1). While climate patterns drove some of the spatial patterns of E : I and τ , variation in lake depth and watershed size (influencing precipitation volume) were also significant drivers. Lake hydrochemistry was strongly correlated to E : I and more weakly related to τ . Lakes in poor biological condition (based on a predictive model of planktonic taxa) were significantly more evaporated than lakes in good biological condition.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.606
Threshold uncertainty score0.229

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.013
GPT teacher head0.287
Teacher spread0.274 · 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 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

Citations176
Published2014
Admission routes1
Has abstractyes

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