Implementing long-term genomic observation in the marine environment: preliminary results from the European Marine Omics Biodiversity Observation Network (EMO BON)
Bibliographic record
Abstract
There are many individual biological observation stations in Europe, however there are few and inconsistent links between them. The European Marine Omics Biodiversity Observation Network (EMO BON) is an ESFRI (European Strategy Forum on Research Infrastructures) initiative, coordinated by the European Marine Biological Resource Centre-European Research Infrastructure Consortium (EMBRC-ERIC) to unite marine stations under one centrally organised observation network that uses shared protocols, international standards and agreed policies.EMO BON is employing omics methodologies for accurate biodiversity monitoring and reporting; it aims to establish a coordinated, long-term, marine biodiversity observation network. It was launched in 2021, and it currently includes 17 marine stations, in 9 countries, ranging from the Arctic to the Red Sea, which regularly collect samples from three different habitats (water column, soft substrates, and hard substrates) and three different communities (microbes, meiofauna and macrofauna).EMO BON generates high-quality FAIR genomic biodiversity data that are being made periodically available to all interested parties and thereby support constructive dialogue towards a holistic understanding of our ocean.EMO BON is an OBON (Ocean Biomolecular Observing Network) endorsed project and thus is it one of the UN Ocean Decade Actions. EMO BON has become the European contribution to the global marine biodiversity observation efforts and plans to collaborate and integrate further with other global entities. Preliminary results, based on 4 TB of data from 700 samples, will be presented as example case studies of the added value of including genomic data into conventional monitoring schemes. As such, EMO BON data could be used to meet the objectives of the Kunming-Montreal Global Biodiversity Framework (GBF) since they can be used to identify the impact of stressors, such as climate change and other human activities, to coastal ecosystems.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".