Sensitivity analysis for climate change impacts, adaptation & mitigation projection with pasture models
Bibliographic record
Abstract
The development of climate adaptation services requires an improved accuracy in model projections for climate change impacts on pastures. Moreover, changes in grassland management need to be tested in terms of their adaptation and mitigation potential. Within AgMIP (Agricultural Model Intercomparison and Improvement Project), based on the C3MP protocol for crops, we explore climate change impacts on future greenhouse gas emissions and removals in temperate grassland systems. Site calibrated models are used to provide projections under probabilistic climate change scenarios, which are defined by a combination of air temperature, precipitation and atmospheric CO2 changes. This design provides a test of yield, greenhouse gas emissions (N2O and CH4) and C sequestration sensitivity to climate change drivers. Moreover, changes in animal stocking density and in grazing vs. cutting are explored to test potential mitigation and adaptation options. This integrated approach has been tested for 12 models applied to 19 grassland sites over three continents and is seen as a pre-requisite for the use of models in the development of climate adaptation and mitigation services for grazing livestock.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".