Gaps in tipping points research across freshwater, marine, and terrestrial ecosystems
Bibliographic record
Abstract
The concept of tipping points is increasingly being addressed in both fundamental and applied environmental contexts, and is particularly salient in the context of anthropogenic threats, including climate change. Most research on tipping points has been conducted through the lens of a single realm (i.e., freshwater, marine, or terrestrial). Yet, there is both the need and opportunity to learn and share across ecosystems, and to engage in coordinated and comparative research. We aimed to identify priority questions that are germane to freshwater, marine and terrestrial realms, and that, if answered, would improve our ability to understand what tipping points are, why they occur, where they occur, and what to do about them. To help enable such efforts, we assembled a team with diverse expertise to identify key research questions, supplemented by an outreach call distributed via various electronic outlets (e.g., email, websites, social media). The responses were then thematized, evaluated, aggregated or disaggregated, and prioritized. Through workshops, and using a modified Delphi approach, we developed a final list of 18 priority research questions. Key themes that emerged included questions of societal relevance (i.e., why questions), drivers, ecological processes, and sensitivity (i.e., what questions), scale and connectivity (i.e., where questions), and tools, techniques, and resources for implementation (i.e., how questions). These questions frame a research agenda intended to help guide future fundamental and applied research related to tipping points in freshwater, marine, and terrestrial ecosystems.
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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 | 0.135 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.016 | 0.022 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.017 | 0.037 |
| Open science | 0.009 | 0.024 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".