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Record W1990790504 · doi:10.1029/2008jc004844

Estuarine forcing of a river plume by river flow and tides

2008· article· en· W1990790504 on OpenAlexaffabout
Mark J. Halverson, Rich Pawlowicz

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEstuaryForcing (mathematics)PlumeGeologyStreamflowOceanographyHydrology (agriculture)Environmental sciencePanacheFlow (mathematics)ClimatologyMeteorologyDrainage basinGeographyGeotechnical engineering

Abstract

fetched live from OpenAlex

Estuarine forcing of a river plume by river discharge and tides is examined with a novel data set capable of characterizing semidiurnal to annual time scales. An instrumented ferry made high‐resolution salinity measurements as it crossed the Fraser River plume, British Columbia, Canada, eight times per day over the years 2003–2006. The relative contribution of different forcing factors in controlling the river plume salinity and surface area is examined over the full range of time scales. A Lomb‐Scargle periodogram of the plume salinity shows energy concentrated in the semidiurnal and diurnal tidal bands. Diurnal lines contain more energy relative to semidiurnal lines than the respective tidal constituents would suggest. At fortnightly frequencies, local maxima in plume salinity coincide with periods of maxima in daily tidal height, with no phase shift. Thus the estuary adjusts quickly to changes in forcing. The effectiveness of tides in setting the plume salinity is a function of river discharge and is greatest when the river discharge is high and minimal when the river discharge is low. Tidal effects are superimposed onto the long‐period river discharge cycle. At time scales of 25 days or longer, the mean river plume salinity decreases quasi‐linearly with increasing river discharge, but the change in salinity with river discharge is instantaneous to within the sampling resolution. Plume surface area increases with river discharge, from 200–500 km 2 at low river flow to 1000–1500 km 2 at high river flow. The magnitude of the surface area is predicted well by scaling the mouth deformation radius.

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 score0.098
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.024
GPT teacher head0.261
Teacher spread0.237 · 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

Citations59
Published2008
Admission routes2
Has abstractyes

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