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Record W2064503332 · doi:10.1071/en07091

Fitzroy River Basin, Queensland, Australia. IV. Identification of flood sediment sources in the Fitzroy River

2008· article· en· W2064503332 on OpenAlexaff
Grant Douglas, Phillip W. Ford, Mark R. Palmer, R. M. Noble, Robert Packett, Evelyn S. Krull

Bibliographic record

VenueEnvironmental Chemistry · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsDepartment of Environment and Conservation
Fundersnot available
KeywordsSedimentHydrology (agriculture)Drainage basinContext (archaeology)Flood mythEnvironmental scienceGeologyFloodplainGeomorphologyGeography

Abstract

fetched live from OpenAlex

Environmental context. During flood events, the Fitzroy River is a major contributor to the loads of suspended sediment and nutrients to the southern Great Barrier Reef. The present geochemical and modelling study provides for the first time a quantitative estimate of the temporal variation in sediment sources over an entire flood hydrograph. Basaltic soils are substantially enriched in this flood event relative to their catchment abundance. Abstract. Suspended sediment collected over a complete flood hydrograph in the Fitzroy River provided an insight into the origin and transport of sediment in this system. Strong temporal trends are evident in the proportions of catchment soil types estimated using a Bayesian mixing model in the fine (<10 µm) fraction of the suspended sediment. These temporal trends were also manifested in changes in mineralogy, major and trace element and Nd–Sr and C–N isotope geochemistry. Tertiary Basaltic soils were the most abundant catchment soil type transported in the flood event studied here, constituting 39% of the <10-µm sediment fraction, but varied between an estimated 20 and 50% of the suspended solids over the course of the flood event. The techniques used here allow quantification and comparison between flow and suspended sediment sources and are widely applicable to other river systems.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.996

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)0.0050.000

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 head0.014
GPT teacher head0.190
Teacher spread0.177 · 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.

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

Citations18
Published2008
Admission routes1
Has abstractyes

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