Nonlinearities in the Earth system
Bibliographic record
Abstract
The complex non-linear physical, chemical, and biological interactions among the components of the Earth System are becoming an increasingly important focus in global change research [1]. These interactions between atmosphere, oceans, ice, and land are driven externally by the solar input of heat, and internally by geologic activity and the myriad processes that control the behaviour of each sub-system (Figure 1). Human activity is an integral component of these interactions. At the 3 rd IGBP Congress, Banff, Canada, a working group entitled “Development of Earth System models to predict non-linear responses/ switches ” was convened to review our understanding of this nonlinear system. The session built upon an earlier IGBP workshop entitled “Non-linear responses to global environmental change: critical thresholds and feedbacks”, held at Duke University, North Carolina, USA, in May 2001. At these meetings, a diverse group of scientists confirmed that each component of the Earth System itself includes complex non-linear feedbacks, in addition to the non-linear interactions between the components. This article draws on the above two meetings to discuss the implications of Earth System complexity for Earth System research, modelling, and prediction. The complexity of the Earth System’s behaviour makes it extremely difficult to accurately forecast the future of the Earth System, and presents a major challenge to the global change research community. New mathematical approaches to assess non-linear behaviour have been explored in recent years to address the problem. Such approaches are taking advantage of advances in the theory of chaotic behaviour and deterministic and stochastic predictability. The goal is to develop techniques for prediction of a system in which many of the components, processes, and thresholds are uncertain or even unknown. As such, one of the main conclusions of the above-mentioned IGBP meetings was the recognition that the evaluation of key vulnerabilities and sensitivities of the Earth System to human
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".