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Record W2975745485 · doi:10.1139/as-2018-0029

Oceanographic, ecological, and socio-economic impacts of an unusual summer storm in the Mackenzie Estuary

2019· article· en· W2975745485 on OpenAlexaffvenue
Kevin Scharffenberg, Dustin Whalen, Shannon A. MacPhee, Marianne Marcoux, John Iacozza, Gail K. Davoren, Lisa L. Loseto

Bibliographic record

VenueArctic Science · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsFisheries and Oceans CanadaGeological Survey of CanadaNatural Resources CanadaUniversity of Manitoba
Fundersnot available
KeywordsBelugaStormEnvironmental scienceStorm surgeEstuaryBeluga WhaleOceanographyArcticFlood mythClimate changeDeltaWinter stormClimatologyGeographyFisheryGeology

Abstract

fetched live from OpenAlex

With increased warming and open water due to climate change, the frequency and intensity of storm surges is expected to increase. Although studies have shown that strong storms can negatively impact Arctic ecosystems, the impact of storms on Arctic marine mammals is relatively unknown. In July 2016, an unusually large storm occurred in the Mackenzie Delta while instrumented seabed moorings equipped with hydrophones and oceanographic sensors were in place to study environmental drivers of beluga habitat use during their summer aggregation. The storm lasted up to 88 h, with maximum wind speeds reaching 60 km/h; historical wind data from Tuktoyaktuk revealed a storm of similar duration has not occurred in July in at least the past 28 years. This provided a unique opportunity to study the impacts of large storms on oceanographic conditions, beluga habitat use, and the traditional subsistence hunt that occurs annually in the delta. The storm resulted in increased water levels and localized flooding as well as a significant drop in water temperature (∼10 °C) and caused belugas to leave the area for 5 days. Although belugas returned after the storm ended, the subsistence hunt was halted resulting in the lowest beluga harvest between 1978 and 2017.

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 score0.004
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.018
GPT teacher head0.264
Teacher spread0.246 · 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

Citations15
Published2019
Admission routes2
Has abstractyes

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