Decoding the ocean's microbiological secrets for marine enzyme biodiscovery
Bibliographic record
Abstract
A global census of marine microbial life has been underway over the past several decades. During this period, there have been scientific breakthroughs in estimating microbial diversity and understanding microbial functioning and ecology. It is estimated that the ocean, covering 71% of the earth's surface with its estimated volume of about 2 × 1018 m3 and an average depth of 3800 m, hosts the largest population of microbes on Earth. More than 2 million eukaryotic and prokaryotic species are thought to thrive both in the ocean and on its surface. Prokaryotic cell abundances can reach densities of up to 1012 cells per millilitre, exceeding eukaryotic densities of around 106 cells per millilitre of seawater. Besides their large numbers and abundance, marine microbial assemblages and their organic catalysts (enzymes) have a largely underestimated value for their use in the development of industrial products and processes. In this perspective article, we identified critical gaps in knowledge and technology to fast-track this development. We provided a general overview of the presumptive microbial assemblages in oceans, and an estimation of what is known and the enzymes that have been currently retrieved. We also discussed recent advances made in this area by the collaborative European Horizon 2020 project 'INMARE'.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".