Vegetation response to varying CO2 conditions during the Mid-Pliocene Warm Period.
Bibliographic record
Abstract
The Mid-Pliocene Warm Period (mPWP) provides a valuable analog for near-future climate warming with an estimated global mean temperature 2.5–4°C higher than today and atmospheric CO₂ concentrations ranging from 360 to 420 ppm. Vegetation changes during the mPWP were significant, playing a crucial role in the climate through feedback mechanisms. Studying the climate-vegetation interactions provides insights into their strength, temporal dynamics, and their role in extreme events. We plan to investigate these interactions by examining vegetation changes under various climate scenarios, including distinct vegetation configurations. As a first step in this research, we will develop a set of vegetation scenarios from exploratory model runs which will then be used as boundary conditions in future runs—in combination with other varying conditions such as varying GHG levels, paleogeography, orbital configurations, and aerosol emissions— to incorporate vegetation dynamics in the mPWP experiments.Here, we present preliminary results regarding the changes in spatial coverage of different vegetation during mPWP scenario runs and our proposed vegetation scenarios. The vegetation scenarios are developed from mPWP simulations with varying atmospheric CO₂ concentrations of 350 ppm, 400 ppm, and 490 ppm. These simulations were performed with the Community Earth System Model version 1.2, a fully coupled climate model, and Biome4, an offline equilibrium vegetation model. We will show the responses of paleo vegetation to climates under different CO₂ levels and quantify the stability of vegetation around the globe within the different scenarios. Based on these results, we will propose a set of vegetation scenarios for use in future studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".