MétaCan
Menu
Back to cohort
Record W2055394012 · doi:10.1121/1.2382719

Linear and nonlinear measures of ocean acoustic environmental sensitivity

2007· article· en· W2055394012 on OpenAlex
Stan E. Dosso, Peter M. Giles, Gary H. Brooke, Diana F. McCammon, Sean Pecknold, Paul C. Hines

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsDefence Research and Development CanadaGeneral Dynamics (Canada)University of Victoria
Fundersnot available
KeywordsSensitivity (control systems)Nonlinear systemSeabedRange (aeronautics)Speed of soundAcousticsUnderwater acousticsEnvironmental scienceBioacousticsMathematicsGeologyPhysicsOceanographyUnderwaterMaterials scienceEngineering

Abstract

fetched live from OpenAlex

This letter defines linear, linearized, and nonlinear measures of environmental sensitivity for ocean acoustic propagation that account for realistic uncertainties in various environmental parameters (water-column sound-speed profile and seabed geoacoustic properties). Simple interpretations of sensitivity are based on the implicit assumption of a linear relationship between parameter sensitivity and parameter uncertainty. This assumption is examined by comparing the three sensitivity measures over a range of parameter uncertainties about the actual assumed environmental uncertainty. Sensitivity range and depth dependencies are illustrated for realistic geoacoustic uncertainties and oceanographic variability of the sound-speed profile.

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.

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)0.002
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score0.596

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.244
Teacher spread0.225 · 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