Generated land systems: recognition and prospects of land system science
Bibliographic record
Abstract
Although land system research has made important progress in land change monitoring, long-term histories of land use change, land system change modeling, and case-study synthesis, it still faces some challenges in system integration and comprehensive analysis. To better understand and explore the comprehensiveness of land systems, system integration theory should be combined with system generation theory that emphasizes historical accumulation. Therefore, this paper revisits some of the basic connotations and theories of land system science by reviewing relevant research and proposes the concept of generated land systems based on system generation theory with an aim to providing a reference base for future research. As coupled human–environment systems are generated by mankind’s transformation, utilization, and adaptation of the land surface and its upper and lower spaces of Earth, generated land systems evolve in the mutual generation and restriction of the biophysical environment, land use, and social economy. The evolution forms of generated land systems can be classified as fluctuation, degradation, and optimization based on the ascendency and resilience of the system. The need for generated land systems to be multi-functional is what motivates the direction and form of generated land system evolution. Generation mechanism, process, adaptability, scale effect, and tele-coupling are important issues of generated land system research. In addition, how generated land systems can enter a new evolutionary cycle through functional transformation is also crucial to achieving sustainable management and utilization of land resources.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".