Estimating global-local dynamics of land use systems by downscaling from GLOBIOM model
Bibliographic record
Abstract
High spatial resolution land use and cover change projections are one of the crucial inputs required by Global Circulation Models. They can be effectively used for the assessment of local carbon and greenhouse-gas (GHG) stocks and fluxes in various ecosystems. However to produce these future land cover maps directly with a global economic land use planning model remains a challenge. Although GIS provides detailed geo-physical information, the socio-economic data on driving forces usually exist only on aggregate level. We propose a model fusion involving two interlinked stages: calculation of regional projections by using a global dynamic model GLOBIOM and proper dynamic downscaling method allowing to obtain the required spatially resolved land use and cover change projections. The proposed procedure allows incorporating data derived from satellite images, statistics, expert opinions, as well as model-derived data from global land use models. The two interlinked models bring consistent results between large scale land use change processes and local dynamics, as illustrated by projections for China, Canada, Brazil, US, Ukraine, Russia. There are connections of proposed entropy-based downscaling approach with a fundamental maximum likelihood method proposed for traditional statistical estimation problems. In many practical applications, available prior distributions may not be known exactly, therefore, we develop a new general probabilistic approach to achieve downscaling results robust with respect to a set of prior distributions. This method generalizes ideas of robust statistics for new estimation problems without real repetitive observations of uncertainties.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".