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Record W4399366521 · doi:10.1080/15715124.2024.2349020

Environmental effects of river ice, The Saint John (Wolastoq) River, New Brunswick, Canada

2024· article· en· W4399366521 on OpenAlexaffabout
Brian C. Burrell, Spyros Beltaos, Brent E. Newton

Bibliographic record

VenueInternational Journal of River Basin Management · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsEnvironment and Climate Change CanadaGovernment of New BrunswickUniversity of Fredericton
Fundersnot available
KeywordsSAINTRiver managementHydrology (agriculture)GeographyArchaeologyGeologyEnvironmental scienceEnvironmental resource managementHistoryGeotechnical engineering

Abstract

fetched live from OpenAlex

The Saint John (Wolastoq) River (SJR) is a major international river flowing through the State of Maine, USA, and the Province of New Brunswick, Canada. In terms of river length and basin area, it is one of the largest rivers on the eastern seaboard of North America south of the St. Lawrence River system. Subject to seasonal ice cover, ice conditions vary along the SJR due to natural differences in climate and terrain, and due to anthropogenetic changes, such as the construction of dams. Ice formation, growth, and breakup along the SJR affects its hydraulic and ecological regimes and leads to the potential for ice jamming that has caused severe flooding and ice runs. Ice affects river erosion, flooding, ecology, water quality, and recreational uses of the SJR. An overview of typical winter climate and ice season characteristics along the SJR from Dickey, Maine, USA to Saint John, New Brunswick, and some of its environmental consequences are summarized in this review paper. Ice processes and phenomena that occur along the SJR occur along other northern rivers, and can cause similar environmental concerns and consequences that should not be ignored in river engineering and basin management.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.624
Threshold uncertainty score0.970

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.003
GPT teacher head0.180
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.

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

Citations1
Published2024
Admission routes2
Has abstractyes

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