Bibliographic record
Abstract
The demand for data and information is driven by the urgency to act on the current planetary crisis. Global goals, high-level political declarations and landmark agreements (e.g. Agenda 2030, Green Deal, the High Seas Convention, the Plastics Convention and the Kummering-Montreal Global Framework Convention, among others) are accelerating the requirement for observation and monitoring to understand the current state of the oceans, identify relationships between parameters and define predictive models. Following issues are of critical importance for the ocean observation community: High-quality data and information to understand the ocean through accurate, accessible and interoperable information. Multi stakeholders’ engagement to ensure inclusion, diversification, and comprehensive spatial and temporal series. Co-design and partnerships to monitor, coordinate, and integrate the data and metadata. The Metrology for Integrated Marine Management and Knowledge-Transfer Network, (MINKE Project) is an EU-funded Horizon 2020 initiative aiming to set up a multi-stakeholder network for ocean infrastructure within a new paradigm of quality data. This dialogue stems from the key issues identified in the Stakeholder Engagement Report and in the Report about Progress Project Meeting (PPM) and the Synergy Session (SS) Workshop. The D4.4. report highlighted that stakeholder engagement plays a key role in implementing the MINKE project because it supports the platform beyond the project life, enabling improvements for future effectiveness steps.
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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.124 | 0.075 |
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".