Author Correction: Tracing two decades of carbon emissions using a network approach
Bibliographic record
Abstract
“This work is supported by the European Union—Horizon 2020 Program under the scheme ‘INFRAIA-01-2018-2019—Integrating Activities for Advanced Communities’, Grant Agreement n. 871042, ‘SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics’ ( http://www.sobigdata.eu ). This work is also supported by the project ‘Network analysis of economic and financial resilience’, Italian DM n. 289, 25-03-2021 (PRO3 Scuole) CUP D67G22000130001. RM acknowledges support from the ‘Programma di Attività Integrata’ (PAI) project ‘Prosociality, Cognition and Peer Effects’ (Pro.Co.P.E.), funded by IMT School for Advanced Studies Lucca. RM is member of the INDAM group “Gruppo Nazionale per l’Analisi Matematica, la Probabilità e le loro Applicazioni (GNAMPA)”.”
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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 | 0.004 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.072 | 0.042 |
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".