MOSJ2016-2020 metadata and association rules
Bibliographic record
Abstract
Supplementary Data of Chapter 8 of the PhD thesis entitled "Novel insights into the Ecology of the Microbial Rare Biosphere in the Arctic Ocean through the combination of Microbiome DNA sequencing and Machine Learning Approaches", by Francisco Pascoal, under the supervision of Professor Catarina Magalhães, Professor Rodrigo Costa, and Professor Paula Branco. This PhD thesis was carried out in the Biology PhD program of the Faculty of Sciences of the University of Porto. The Supplementary Data made available in here refers to: Supplementary Data 8.1. Metadata of the samples from the Environmental Monitoring of Svalbard and Jan Mayen (MOSJ) program, with data ranging from 2016 to 2020, focusing specifically on the Kongsfjorden transect. This metadata was based both on Pascoal et al., (2025) and Wold et al., (2024). File name: "Metadata_MOSJ2016_2020_Pascoal_et_al_2025.xlsx" Supplementary Data 8.2. Association rules obtained from the association rule mining algorithm. File name: "Dependent_rules_Pascoal_et_al_2025.xlsx" References: Pascoal, F. et al. (2025) “Definition of the microbial rare biosphere through unsupervised machine learning,” Communications Biology (in peer-review) [Preprint]. Wold, A., Assmy, P. and Duarte, P. (2024) “ Biogeochemistry data from Kongsfjorden and Rijpfjorden 2011 to 2020 [Data set].” Norwegian Polar Institute.
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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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.410 | 0.276 |
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".