Insights into greener Miocene biomes and globally enhanced terrestrial productivity from fossil leaves
Bibliographic record
Abstract
Leaf megafloras represent a snapshot of local environmental conditions in Earth's history. As such, they are an important way to understand terrestrial climate analogues for future warmer climate scenarios. Here, we present paleoclimate, productivity, and biome reconstructions of 108 globally distributed Miocene leaf megafloras using a standardized method based on leaf physiognomy. Our results show that the Miocene had higher than modern zonal temperature, precipitation and net primary productivity (NPP) averages, especially for precipitation at latitudes >30°N/°S, suggesting enhanced poleward moisture transport in both hemispheres and a greener biosphere. There is a dearth of Miocene data in the tropics and notably an absence of data points in equatorial localities that have high modern NPP (rainforests), which makes a direct comparison complicated. 89% of investigated sites underwent a precipitation decrease from the Miocene to modern, whereas 66% underwent a temperature decrease, and 60% underwent both a precipitation and a temperature decrease. 67% of sites had more productive biomes during the Miocene than today. Most notably, forested biomes were replaced by more open woodland/shrubland or grassland biomes. Correspondingly, the average NPP decrease from the Miocene to today of our investigated localities was conservatively ∼250 gC m−2 yr−1 or ∼450 gC m−2 yr−1 by comparison of zonal averages. Considered collectively, leaf megafloras reveal an overall greener Miocene world that appears to be largely driven by greater moisture availability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".