Toward integrated historical climate research: the example of Atmospheric Circulation Reconstructions over the Earth
Bibliographic record
Abstract
Climate change has become a key environmental narrative of the 21st century. However, emphasis on the science of climate change has overshadowed studies focusing on human interpretations of climate history, of adaptation and resilience, and of explorations of the institutions and cultural coping strategies that may have helped people adapt to climate changes in the past. Moreover, although the idea of climate change has been subject to considerable scrutiny by the physical sciences, recent climate scholarship has highlighted the need for a re‐examination of the cultural and spatial dimensions of climate, with contributions from the humanities and social sciences. Establishing a multidisciplinary dialogue and approach to climate research past, present, and future has arguably never been more important. This article outlines developments in historical climatology research and considers examples of integrated multidisciplinary approaches to climate, climatic variability, and climate change research, conducted across the physical sciences, social sciences, humanities, and the arts. We highlight the international Atmospheric Circulation Reconstructions over the Earth ( ACRE ) initiative as one example of such an integrated approach. Initially, ACRE began as a response from climate science to the needs of the agricultural sector in Queensland, Australia for a longer, more spatially, and temporally‐complete database of the weather. ACRE has now evolved to embrace an international group of researchers working together across disciplines to integrate their efforts into a four‐dimensional ( 4D ) dynamical global historical climate‐quality reanalysis (reconstruction). WIREs Clim Change 2016, 7:164–174. doi: 10.1002/wcc.379 This article is categorized under: Climate, History, Society, Culture > Disciplinary Perspectives Paleoclimates and Current Trends > Modern Climate Change
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".