Strengthening mycology research through coordinated access to microbial culture collection strains
Bibliographic record
Abstract
Abstract Well-managed genetic resources and associated metadata are essential to underpin research addressing the challenges to food security, healthcare, climate change, biodiversity, environment, education and our bio-based economy. Culture collections have supported microbiology research for over 100 years, whether they are collections belonging to individual scientists or institutional repositories. The 790 collections registered with the World Data Centre for Microorganisms (WDCM) together hold over three million strains representing a wide range of microbial diversity. This review provides an overview of the uses and outputs of collections that support work in mycology, agriculture and the environment. Further, it focusses on the advantages of coordinating efforts and establishes recommendations to improve resource provisions for research and the development of the necessary infrastructure. The CABI living resource collection provides an example that holds over 28,000 strains of fungi from 100 years of research in mycology. In the modern era, microbial interventions and solutions require knowledge not only of those microorganisms that can be grown and preserved axenically but also whole microbial communities: i.e. ‘microbiomes’. Current technologies enable us to access this latter, hidden resource, thereby facilitating a better understanding of how to harness and manipulate microbial communities to improve crop yields and allow successful interventions such as biocontrol of pests, diseases and invasive species. The WDCM Analyzer of Bio-resource Citations reports that 79,224 strains from 131 collections from 50 countries have been cited in 145,133 papers published in 50,307 journals from January, 1953 until April, 2020. These organisms have a multitude of uses, for example as sources of antibiotics, therapeutic drugs and other active agents. They have been applied widely including in the biodegradation, bioremediation, biotransformation and biotreatment of wastes. Further uses include interventions in agriculture for soil and plant health or biological control of pests and diseases. All of the above may be achieved by individual institutions but, by working together, collections can form a critical mass to focus on key global issues and can achieve much more. Mechanisms are suggested for coordinating collections in order to deliver a more comprehensive support system in the advancement of science and innovation.
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 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 | 0.139 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.030 | 0.025 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.011 |
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".