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Record W1936904133 · doi:10.1016/s0967-0653(98)80017-3

10.1016/s0967-0653(98)80017-3

2000· article· en· W1936904133 on OpenAlexvenueno aff
Eric Wolanski, Brian King, D. K. Galloway

Bibliographic record

VenueTime to knit · 2000
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsnot available
Fundersnot available
KeywordsEstuaryGeologyShoalDischargeTidal irrigationOceanographyWaves and shallow waterHydrology (agriculture)SinuosityGeomorphologyDrainage basinGeotechnical engineering

Abstract

fetched live from OpenAlex

Intensive field and model studies were undertaken into the dynamics of the Fly River estuary, Papua New Guinea. The estuary has three dominant channels forming a shallow, fan-shaped delta, and receives a mean freshwater discharge of approximately 6,000 m 3 s -1 with little seasonal variation. The estuary is vertically well-mixed in salinity by strong tidal currents. The saline water is distributed unevenly between the channels. Model studies verified by field data suggest that this due to the dynamics of the estuary which are controlled by shallow water frictional effects that generate higher tidal harmonics, the shoaling of the tidal wave from the funnel shape of the estuary, a low value of the bottom friction coefficient resulting from the presence of fluid mud, and the along-channel water surface gradient. This gradient is in turn controlled by two dominant forcings, namely the freshwater discharge and the dominant offshore trade wind. This gradient is also modulated by the spring-neap cycle of the tidal currents which controls the low-frequency friction coefficient. The absence of strong cross-channel salinity gradients and of axial convergence zones is attributed to enhanced horizontal mixing by the lateral velocity shear due to the sinuosity of the thalweg meandering between numerous islands and shoals.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.891
Threshold uncertainty score0.254

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)1.0001.000

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 head0.009
GPT teacher head0.166
Teacher spread0.157 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations40
Published2000
Admission routes1
Has abstractyes

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