MétaCan
Menu
Back to cohort
Record W6908473737 · doi:10.26186/149680

Forster (Cape Hawke to Black Head), NSW Bathymetry Acquisition (20190018S)

2024· dataset· en· W6908473737 on OpenAlexaboutno aff

Bibliographic record

VenueGeoscience Australia · 2024
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsBathymetryGovernment (linguistics)Baseline (sea)CapeNova scotiaGlobal Positioning SystemCoastal management

Abstract

fetched live from OpenAlex

The Forster, Cape Hawke to Black Head, bathymetry survey was acquired by the NSW government (Department of Planning and Environment – DPE) onboard the RV Bombora during the period 27 Feb 2019 – 14 Oct 2020, using DPE’s R2Sonic 2022 multibeam sonar. The survey was completed as part of the SeabedNSW program funded by NSW government through Coastal Reforms (>2015), HabMap Program funded through Marine Parks Authority (now under Marine Estate Management Authority) or through collaborations with partner agencies or institutions. The purpose of the project was to 1) provide a baseline dataset and 2) map the spatial distribution of seabed types. This dataset contains 32-bit floating point geotiff files of bathymetry and backscatter in 5m resolution for the study area, derived from the processed Hypack, R2Sonic GUI, POSView, POSPac, Qimera and FMGT software. General details on vessel setup, mobilisation and processing are provided at https://www.environment.nsw.gov.au/-/media/OEH/Corporate-Site/Documents/Research/Our-science-and-research/seabed-nsw-standard-operating-procedures-multibeam-surveying-190101.pdf with survey specific details in the Survey Report and DPIE Rigor Statement (can be provided upon request).This dataset is not to be used for navigational purposes.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.686
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.695

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 head0.056
GPT teacher head0.359
Teacher spread0.303 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueGeoscience AustraliaFrench-language works237,207