Bibliographic record
Abstract
Land use and land cover change(LUCC) is a major part and also a main cause of global environmental changes,and it has emerged recently as an important focus for land change studies.Based on the systematic summary of the progress of studies in LUCC in the latest decade,including its theories,methods and applications,a series of problems that should be urgently resolved in the study are put forward,and some important study directions and priorities for future are reviewed.Results show that LUCC model plays an important role and is an efficient tool to support the analysis of the causes,processes and consequences of land use systems and to support land use planning and policy.Second,spatio-temporal patterns of LUCC are the research core of LUCC models.The development of models has experienced an evolvement from single non-spatial to the combination of non-spatial and spatial models,however,at present most models are static models and ignore the temporal dimension of land-use change.Third,feedback is one of the important characteristics of LUCC;however,the majority of the existing LUCC models are very weak in analyzing and presenting the feedbacks of LUCC.In this regard,how to get a better understanding of the feedbacks at different time and space scales will be one of new tasks in LUCC models.Fourth,the objective of LUCC models is to study the dynamic relations of a coupled human-environment.Currently,most LUCC models are partial-equilibrium ones.Future LUCC models will focus on studies on the human-environment system from a systematic and holistic point of view.Fifth,multi-scale analysis in LUCC models is needed for a better understanding of land use change.Early LUCC models used to take a single scale or level of analysis into account.Recently,a number of LUCC models which implement multiple scales can be distinguished.The scaling will be a key issue in future LUCC models.Finally,although many methods of model validation are available,there is not a uniform standard and criterion of model validation.The weakness in reference data also limits the performance of model validation.All these will challenge the development of future LUCC models.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".