MétaCan
Menu
Back to cohort
Record W2297786656

ICEBERG RISK TO SEABED INSTALLATIONS ON THE GRAND BANKS

2001· article· en· W2297786656 on OpenAlexfundvenueno aff
Ken Croasdale, Rob Brown, Patrick Campbell, Greg Crocker, Ian Jordaan, Tony King, Richard McKenna, Robert F. Myers

Bibliographic record

VenueNPARC · 2001
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsnot available
FundersNational Research Council Canada
KeywordsIcebergBathymetrySeabedDocumentationEvent (particle physics)CrewDuration (music)GeologyMarine engineeringOceanographyEngineeringSea iceAeronauticsComputer science
DOInot available

Abstract

fetched live from OpenAlex

Nine icebergs were reported to have grounded on the northeastern Grand Banks during the year 2000 ice season. Documentation of possible groundings provides a basis for planning seabed scour surveys and thus the ability to study the degradation of scours over time,knowing the exact date of scour creation. All available data for the nine reported grounding events were examined and are summarized in this paper. These data include photographs,iceberg principal dimensions, iceberg drift tracks, seabed bathymetry, winds, currents, ice management tow force and direction and comments made by crew and ice observers. The datawere then used to prioritize each of the events for the likelihood of finding a scour mark during field surveys.The prioritization was based on five criteria: event duration, local bathymetry, environmental driving forces, crew and observer comments and presence of older, previously measured scours in the area. Modelling of the scour process was conducted in an attempt to estimate scour length and depth for each event. These modelling efforts were
\ncompleted in an attempt to help validate the scour model, as well as to provide furtherinformation on the likelihood of locating reported scours.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.349
Threshold uncertainty score0.999

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)0.0130.002

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 head0.031
GPT teacher head0.253
Teacher spread0.221 · 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; both teacher heads agree on what is shown here.

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

Citations5
Published2001
Admission routes2
Has abstractyes

Explore more

Same venueNPARCSame topicUnderwater Acoustics ResearchFrench-language works237,207