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Record W2085979402 · doi:10.1080/02626660109492837

Tropical cyclones and floods in Fiji

2001· article· en· W2085979402 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueHydrological Sciences Journal · 2001
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of the Pacific
KeywordsTropical cycloneTropical cyclone rainfall forecastingFlood mythClimatologyPrecipitationEnvironmental scienceEl Niño Southern OscillationGeographyHydrology (agriculture)Cyclone (programming language)MeteorologyGeology

Abstract

fetched live from OpenAlex

Abstract Daily flow records, rainfall data and tropical cyclone maps during 1970–1998 are used to document the impact of tropical cyclones (TCs) on floods in the Rewa River system, Viti Levu, Fiji. Floods are large, brief, isolated events caused by TCs and non-TC tropical rainstorms. More floods are caused by tropical rainstorms than by TCs, but TC floods are larger. The log Pearson Type III distribution consistently provided the best fit to partial duration flood series and the widely-recommended generalized Pareto distribution performed very poorly, underscoring the need to test a variety of distributions for a particular geographic location. Tropical cyclones occur more often in Fiji during negative values of the Southern Oscillation Index (SOI) and all TCs that occurred during El Niño conditions caused floods. Peak flood discharges caused by TCs are inversely correlated with the SOI, reflecting possible links with tropical cyclone frequency and precipitation intensity.

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.

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 score0.040
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.035
GPT teacher head0.271
Teacher spread0.236 · 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