Foresight Workshop on Advances in Ocean Biological Observations: a sustained system for deep-ocean meroplankton
Bibliographic record
Abstract
Recent advances in technology have enabled an unprecedented development of underwater research, extending from near shore to the deepest regions of the globe. However, monitoring of biodiversity is not fully implemented in political agendas and biological observations in the deep ocean have been even more limited in space and time. The Foresight Workshop on Advances in Ocean Biological Observations: a sustained system for deep-ocean meroplankton was convened to to foster advances in the knowledge on deep-ocean invertebrate larval distributions and improve our understanding of fundamental deep-ocean ecological processes such as connectivity and resilience of benthic communities to natural and human-induced disturbance. This Meroplankton Observations Workshop had two specific goals: 1) review the state-of-the-art instrumentation available for meroplankton observations; 2) develop a strategy to implement technological innovations for in-situ meroplankton observation. Presentations and discussions are summarised in this report covering: i) key challenges and priorities for advancing the knowledge of deep-sea larval diversity and distribution: ii) recent developments in technology and future needs for plankton observation, iii) data integration and oceanographic modelling; iv) synergies and added value of a sustained observation system for meroplankton; v) steps for developing a sustained observation system for deep-ocean meroplankton and plans to maximise collaborative opportunities.
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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 | 0.035 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".