Development and testing of scenarios for implementing Holocene LULC in Earth System Model Experiments
Bibliographic record
Abstract
Abstract. Anthropogenic changes in land use and land cover (LULC) during the pre-industrial Holocene could have affected regional and global climate. Current LULC scenarios are based on relatively simple assumptions and highly uncertain estimates of population changes through time. Archaeological and palaeoenvironmental reconstructions have the potential to refine these assumptions and estimates. The Past Global Changes (PAGES) LandCover6k initiative is working towards improved reconstructions of LULC globally. In this paper, we document the types of archaeological data that are being collated and how they will be used to improve LULC reconstructions. Given the large methodological uncertainties involved, we propose methods to evaluate the revised scenarios by using independent pollen-based reconstructions of land cover and of climate. A further test involves carbon-cycle simulations to determine whether the LULC reconstructions are consistent with constraints provided by ice-core records of CO2 evolution and modern-day LULC. Finally, we outline a protocol for using the improved LULC reconstructions in palaeoclimate simulations within the framework of the Palaeoclimate Modelling Intercomparison Project in order to quantify the magnitude of anthropogenic impacts on climate through time and ultimately to improve the realism of Holocene climate simulations.
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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 | 0.024 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".