Discovery pipelines for marine resources: an ocean of opportunity for biotechnology?
Bibliographic record
Abstract
Marine microbial diversity offers enormous potential for discovery of compounds of crucial importance in healthcare, food security and bioindustry. However, access to it has been hampered by the difficulty of accessing and growing the organisms for study. The discovery and exploitation of marine bioproducts for research and commercial development requires state-of-the-art technologies and innovative approaches. Technologies and approaches are advancing rapidly and keeping pace is expensive and time consuming. There is a pressing need for clear guidance that will allow researchers to operate in a way that enables the optimal return on their efforts whilst being fully compliant with the current regulatory framework. One major initiative launched to achieve this, has been the advent of European Research Infrastructures. Research Infrastructures (RI) and associated centres of excellence currently build harmonized multidisciplinary workflows that support academic and private sector users. The European Marine Biological Research Infrastructure Cluster (EMBRIC) has brought together six such RIs in a European project to promote the blue bio-economy. The overarching objective is to develop coherent chains of high-quality services for access to biological, analytical and data resources providing improvements in the throughput and efficiency of workflows for discovery of novel marine products. In order to test the efficiency of this prototype pipeline for discovery, 248 rarely-grown organisms were isolated and analysed, some extracts demonstrated interesting biochemical properties and are currently undergoing further analysis. EMBRIC has established an overarching and operational structure to facilitate the integration of the multidisciplinary value chains of services to access such resources whilst enabling critical mass to focus on problem resolution.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".