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Record W2900112800 · doi:10.12716/1001.12.03.04

Iceberg Melting and Climate Change in NW Atlantic Waters

2018· article· en· W2900112800 on OpenAlexaboutno aff
Sergio E. Perez-Gruszkiewicz, W. K. Peterson

Bibliographic record

VenueTransNav the International Journal on Marine Navigation and Safety of Sea Transportation · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsIcebergOceanographyClimate changeGeologyEnvironmental scienceClimatologyIce sheet

Abstract

fetched live from OpenAlex

Climate change is predicted to cause increases in sea surface temperature (SST), as well as decreases in sea-ice cover, wind and current velocities. These changes will have a marked effect on iceberg melting in the shipping lanes off Newfoundland and Labrador, Canada. Icebergs that today can cross from northern Labrador to Newfoundland without melting will in the future have to be much larger to survive the transit. For example, icebergs at N Labrador in December of 2016 that are smaller than 156 m will melt before reaching 48N, but in year 2100 the length increases to 228 m. In addition, if future iceberg size distributions off Labrador are the same as today, icebergs will experience roughly 50% reductions in numbers in the NW Atlantic shipping lanes by year 2100. The increased melting rates are due to, in order of importance, increased sea-surface temperatures (responsible for 66% of the increase in the minimum transit size), decreasing current velocities (31%), and decreasing sea-ice cover (3%). Decreasing sea-ice tends to increase wave heights as well as accelerate the effects of wave erosion; however, for the areas studied the wave height is predicted to decrease moderately in year 2100, by a maximum of about 10% in December.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.261

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)0.0000.000

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 head0.012
GPT teacher head0.240
Teacher spread0.228 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

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