Dataset for: Self-resistance allows lichens to use toxins for UV protection
Bibliographic record
Abstract
# Self-resistance allows lichens to use toxins for UV protectionThis repository contains the code associated to the paper: Self-resistance allows lichens to use toxins for UV protection **Authors**: Theo Llewellyn1,2,3,*, Thomas A.K. Prescott1, Pierre Le Pogam4, Damien Olivier-Jimenez5, Anthony Maxwell6, Rui Fang1, Valerij Talagayev7, Gerhard Wolber7, Alejandro Huereca8, François Lutzoni9, Timothy G. Barraclough2,10, Ester Gaya1 **Affiliations** 1. Department of Trait Diversity and Function, Royal Botanic Gardens, Kew, Richmond, TW9 3DS, UK2. Department of Life Sciences, Imperial College London, Silwood Park Campus, Ascot, Berkshire, SL5 7PY, UK3. Science and Solutions for a Changing Planet Doctoral Training Partnership, Grantham Institute, Imperial College London, South Kensington, London, SW7 2AZ, UK4. UMR CNRS 8076 BioCIS, Université Paris-Saclay5. Université de Rennes, CNRS, ISCR UMR 6226, F-35000 Rennes, France6. Department of Biochemistry and Metabolism, John Innes Centre, Norwich Research Park, Norwich NR4 7UH, UK7. Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, Freie Universität Berlin, Königin-Luise-Str. 2+4, Berlin 14195, Germany8. Department of Biological Sciences CW405, University of Alberta, Edmonton, AB T6G 2R3, Canada9. Department of Biology, Duke University, Durham, NC 27708, USA10. Department of Biology, University of Oxford, 11a Mansfield Road, Oxford, OX1 3SZ, UK *Correspondence: t.llewellyn19@imperial.ac.uk ## Data Records The processed sequence data and metadata are available on the public NCBI SRA under BioProject accession PRJNA991097. All other data files are available here.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.115 | 0.114 |
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".