Accelerating ocean species discovery and laying the foundations for the future of marine biodiversity research and monitoring
Bibliographic record
Abstract
Ocean Census is a new Large-Scale Strategic Science Mission aimed at accelerating the discovery and description of marine species. This mission addresses the knowledge gap of the diversity and distribution of marine life whereby of an estimated 1 million to 2 million species of marine life between 75% to 90% remain undescribed to date. Without improved knowledge of marine biodiversity, tackling the decline and eventual extinction of many marine species will not be possible. The marine biota has evolved over 4 billion years and includes many branches of the tree of life that do not exist on land or in freshwater. Understanding what is in the ocean and where it lives is fundamental science, which is required to understand how the ocean works, the direct and indirect benefits it provides to society and how human impacts can be reduced and managed to ensure marine ecosystems remain healthy. We describe a strategy to accelerate the rate of ocean species discovery by: 1) employing consistent standards for digitisation of species data to broaden access to biodiversity knowledge and enabling cybertaxonomy; 2) establishing new working practices and adopting advanced technologies to accelerate taxonomy; 3) building the capacity of stakeholders to undertake taxonomic and biodiversity research and capacity development, especially targeted at low- and middle-income countries (LMICs) so they can better assess and manage life in their waters and contribute to global biodiversity knowledge; and 4) increasing observational coverage on dedicated expeditions. Ocean Census, is conceived as a global open network of scientists anchored by Biodiversity Centres in developed countries and LMICs. Through a collaborative approach, including co-production of science with LMICs, and by working with funding partners, Ocean Census will focus and grow current efforts to discover ocean life globally, and permanently transform our ability to document, describe and safeguard marine species.
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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 | 0.022 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".