Quantifying Earth system interactions for sustainable food production: an expert elicitation
Bibliographic record
Abstract
Abstract Several safe boundaries of critical Earth system processes have already been crossed by human perturbations. Recent research indicates that not accounting for the interactions between these processes may further narrow the safe operating space for humanity. Yet existing work accounts only for transgression of single boundaries and only a few studies take some of the boundary interactions into account. For future sustainability assessments, it is essential to understand boundary transgressions and their interactions more comprehensively. Here, we explore quantitatively how strongly seven variables, representing Earth system processes relevant to food production, interact with each other, using a structured expert knowledge elicitation. We identify Green water and Land system change as crucial interactive processes through their impacts on multiple relevant processes, while Biosphere integrity-land, freshwater and ocean components appear to be most affected by other Earth system processes, most notably Blue water and Biogeochemical flows. The elicitation also enabled us to map the complex network of mechanisms mediating interactions, to support integrated Earth system and planetary boundaries modelling and assessments. Finally, we created a prioritisation scheme for future research according to the interaction strengths and existing knowledge gaps. Our analysis improves our understanding of Earth system interactions, with clear implications for sustainable use of natural resources such as the biophysical limits for food production.
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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 | 0.013 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".