Goal and products of PAGES LandCover6k 2018-2020: Past Global Land Cover and Land Use for Climate Modelling
Bibliographic record
Abstract
The question of whether prehistoric human impacts on land cover (i.e. anthropogenic land cover change due to land use, LULC) were sufficiently large to have a major impact on regional cli-mates is still a matter of debate. Climate model simulations have shown that LULC datasets can have large regional impacts on climate in recent and prehistoric time (1). But there are major differences between the available LULC scenarios/datasets such as HYDE (History Database of the Global En-vironment) and Kaplan’s KK10 (2), and diagnoses of inferred carbon-cycle impacts show that none of the scenarios are realistic (3). The only way to provide a useful assessment of the potential for LULC changes to affect climate in the past, is to provide more realistic LULC data based on palaeovegetation and archaeological evidence to improve the LULC datasets used in climate modelling(4). We use the REVEALS model to reconstruct LC from pollen data at a regional scale, and archaeological data to map LU types and distribution, and estimate per capita LU. The archaeology-based LU maps and per-capita LU estimates are used to improve LULC datasets. Pollen-based REVEALS LC estimates are then used to evaluate/validate the new, improved LULC datasets. These new datasets will be used to implement past land use in palaeoclimate and carbon cycle model simulations. Such simulations are necessary to assess the impact of LULC changes in the past and understand the effect of ecosys-tem management on future climate. We present results from five years of PAGES LandCover6k activities. (1) Strandberg G, Kjellström E, Poska A, Wagner S, Gaillard M-J et al. (2014) Regional climate model sim-ulations for Europe at 6 and 0.2 k BP: sensitivity to changes in anthropogenic deforestation. Clim. Past 10, 661–680. (2) Gaillard M-J, Sugita S, Mazier F et al (2010) Holocene land-cover reconstructions for studies on land cover-climate feedbacks. Clim. Past 6, 483-499. (3) Stocker B, Yud Z, Massae C, Joos F (2017) Holocene peatland and ice-core data constraints on the tim-ing and magnitude of CO2 emissions from past land use. www.pnas.org/cgi/doi/10.1073/ pnas.1613889114. (4) Harrison S P, Gaillard M-J, Stocker B D, Vander Linden M, Klein Goldewijk K, Boles O, Braconnot P, Dawson A, Fluet-Chouinard E, Kaplan J O, Kastner T, Pausata F S R, Robinson E, Whitehouse N J, Madella M, and Morrison K D (2019) Development and testing of scenarios for implementing Holocene LULC in Earth Sys-tem Model Experiments, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-125, in review, 2019.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.032 |
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".