Global patterns of nonanalogous climates in the past and future derived from thermal and hydraulic factors
Bibliographic record
Abstract
Nonanalogous climates (NACs), climates without modern analogs on Earth, challenge our understanding of eco-evolutionary processes that shape global biodiversity, particularly because of their propensity to promote novel ecosystems. However, NAC studies are generally inadequate and partial. Specifically, systematic comparisons between the future and the past are generally lacking, and hydraulic NACs tend to be underemphasized. In the present study, by adopting a frequency distribution-based method that facilitates the procedures of contributions parsing and conducting multiple comparisons, we provide a global overview of multidimensional NACs for both the past and the future within a unified framework. We show that NACs are globally prevalent, covering roughly half of the land area across the time-periods under investigation, and have a high degree of spatial structure. Patterns of NACs differ dramatically between the past and the future. Hydraulic NACs are more complex both in spatial patterns and in major contributions of variables than are thermal NACs. However, hydraulic NACs are more predictable than originally thought. Generally, hydraulic NACs in the future (2100 AD) exhibit comparable predictability to thermal NACs in the last glacial maximum (LGM) (21k BP). Identifying these NAC patterns has potential implications on climate-adaptive managements and preparing in advance to possibly frequent novel ecosystems. However, a learning-from-the-past strategy might be of limited utility for management under present circumstances.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".