FOSIC2020: Science and Technology in Combating Current and Future Global Challenges (LASU Virtual Conference, 2- 4 December 2020)
Bibliographic record
Abstract
The Lagos State University 7th bi-annual Faculty of Science International Conference 2020 tagged LASU FOSIC2020 was held virtually from 2nd-4th December, 2020. The theme of the conference was Science and Technology in combating current and future global challenges. To justify the theme, different sub-themes were combined cutting across biological/medical, chemical and physical sciences including: global ecology and challenges of combating infectious human and zoonotic diseases, emerging perspectives on epidemiology of infectious diseases, post COVID-19 effects on fisheries and aquaculture, molecular approaches in curtailing the scourge of diseases, chemistry of natural resources for sustainable product development, medicinal plants as antidotes, dynamical system analysis, modelling and optimization, artificial intelligence in the 4th industrial revolution, and demystifying 5G technology: the role of physics in tackling global health challenges. This summary therefore presents some of the observations raised at the conference. Topical models and practical strategies at flattening the curve of COVID-19 pandemic in African most populous city, Lagos was presented by the Deputy Governor of Lagos State, while Director General of the Nigerian Institute for Medical Research delivered the keynote address followed by the special guest speaker from Harvard Medical School and Massachusetts General hospital, USA amongst others. To the best of our knowledge, FOSIC2020 was the first free and 100% virtual international conference organised by any Nigerian University to date. Overall, a total of 130 papers were presented by researchers out of the 334 registered participants representing 36 institutions from 14 countries across the world. FOSIC2020 was declared closed with a free technical workshop focusing on V2V global partnership from vulnerability to viability project by the team leaders from the University of Waterloo Canada and Lagos State University with members of panel as postgraduate students across different countries. Free electronics book of abstracts and certificates were given to all the participants.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.088 | 0.027 |
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".