Bibliographic record
Abstract
This Vision Statement arises from the activities of the Atlantic Seabed Mapping International Work \nGroup (hereafter referred to as Seabed Mapping Group), and is conducted through the Atlantic Ocean \nResearch Alliance (AORA) between Canada, the European Union and the United States of America. \nThe progress and vision towards achieving a baseline seabed and habitat map of the Atlantic Ocean, \nwas presented at the All Atlantic Ocean Research Forum, 6-7 February, 2020, in Brussels, Belgium. \nA diverse group of stakeholders participated in this work and the outcome summarised here is a result \nof extensive consultation with workshop and meeting participants, as well as others that were invited to \ncomment on the work as it progressed. \nThe Seabed Mapping Group has, in the last five years, defined and tested all the necessary steps to map the \npreviously uncharted seafloor of the Atlantic Ocean. With the onset of the UN Decade of Ocean Science \nfor Sustainable Development, the Seabed Mapping Group calls on the international leaders to provide the \nresources and framework necessary to achieve this ambitious goal, in order to deliver on their commitment \nto the Galway and Belém Statements. Creating an accurate fact based map of the Atlantic seafloor is essential \nfor the sustainable use of our ocean, and will greatly help us to achieve the UN Sustainable Development Goal.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.024 |
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".