Architecture and implementation of ulrb algorithm in R, source data
Bibliographic record
Abstract
This dataset makes available the source data used for all analyses made in the original research article entitled "Architecture and implementation of ulrb algorithm in R", for the journal Ecological Informatics. Short description of files: nice_ASVs.csv - ASV abundance table in long format; nice_otu_long - OTU abundance table in long format; nice_otu_wide - OTU abundance table in wide format. All files correspond to samples collected from seawater of the Arctic Ocean, during the Norwegian Young Sea Ice Expedition, using V4V5 16S rRNA gene amplicon sequencing. To use this dataset, please cite: - Pascoal, F. et al. (2025) “Definition of the microbial rare biosphere through unsupervised machine learning,” Communications Biology, 8(1), p. 544. Available at: https://doi.org/10.1038/s42003-025-07912-4. - Pascoal, F. et al. (2022) “Exploration of the Types of Rarity in the Arctic Ocean from the Perspective of Multiple Methodologies,” Microbial Ecology, 84(1), pp. 59–72. Available at: https://doi.org/10.1007/s00248-021-01821-9. - de Sousa, A.G.G. et al. (2019) “Diversity and Composition of Pelagic Prokaryotic and Protist Communities in a Thin Arctic Sea-Ice Regime,” Microbial Ecology, 78(2), pp. 388–408. Available at: https://doi.org/10.1007/s00248-018-01314-2. - Granskog, M.A. et al. (2018) “Atmosphere-Ice-Ocean-Ecosystem Processes in a Thinner Arctic Sea Ice Regime: The Norwegian Young Sea ICE (N-ICE2015) Expedition,” Journal of Geophysical Research: Oceans, 123(3), pp. 1586–1594. Available at: https://doi.org/10.1002/2017JC013328.
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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.079 | 0.115 |
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".