Toward integrated historical climate research: the example of Atmospheric Circulation Reconstructions over the Earth
Bibliographic record
Abstract
[Article in WIREs Clim Change 2016, 7:164–174. doi: 10.1002/wcc.379] The following funding grant for co-author Professor Lorna Hughes was omitted in the Acknowledgement section: AHRC grant award reference AH/K502765/1, held at the University of Wales: http://gtr.rcuk.ac.uk/projects?ref=AH/K502765/1. The full Acknowledgments should reads as: The lead author is supported by a combination of funding from the Joint DECC/Defra Met Office Hadley Centre Climate Programme (GA01101), the European Union's Seventh Framework Programme (FP7) European Reanalysis of Global Climate Observations 2 (ERA-CLIM2) project and the Climate Science for Service Partnership (CSSP) China under the Newton Fund. The Nottingham based project exploring Documentary reconstructions of extreme weather events in the UK, past, present and future, is funded through the AHRC, Grant number: AH/K005782/1. 'The Snows of Yesteryear: Narrating Extreme Weather' Project, based at the University of Wales, was funded by the AHRC, award number AH/K502765/1.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".