Forster (Cape Hawke to Black Head), NSW Bathymetry Acquisition (20190018S)
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.057 | 0.038 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".