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Record W3109536265 · doi:10.1121/1.5146741

A new measurement of the deepest depth of the ocean

2020· article· en· W3109536265 on OpenAlexaff
Scott Loranger, David R. Barclay, Michael J. Buckingham

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsGeologySeafloor spreadingTrenchDeep seaOceanographyBathymetryDepth soundingSound (geography)SeabedMeasured depthExplosive materialEcho soundingRemotely operated vehicleSeismologyGeophysicsArchaeologyGeography

Abstract

fetched live from OpenAlex

Scientists and explorers have been searching to determine the exact location and depth of the deepest part of the ocean since the HMS Challenger made the first sounding of the Mariana Trench in 1875. The consensus is that the deepest abyss in the ocean is in the Challenger Deep, a portion of the Mariana Trench with depths greater than 10 000 m. Since the HMS Challenger II returned to the Mariana Trench in 1952, 14 estimates of the deepest depth of the ocean have been made. Estimates of the location and maximum depth are as diverse as the methods used including wire soundings, explosives, single and multibeam sonars, and remotely operated and manned submersibles. During an Office of Naval Research supported expedition to the Challenger Deep in 2014, two free falling passive acoustic instruments were deployed. The implosion of one instrument was recorded by the other when both were at depths greater than 8000 m. Multiple reflections from the seafloor and sea surface of the sound generated by the implosion were used to determine the depth of the Challenger Deep. The result was the most constrained estimate of the deepest part of the ocean, 10 991 ± 6 m.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.038
GPT teacher head0.244
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), 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
Published2020
Admission routes1
Has abstractyes

Explore more

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