Classification of Landscape Types Based on Land Cover, Successional Stages and Plant Functional Groups in a Species-Rich Forest in Hainan Island, China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Large-scale identification of landscape types in species-rich forest ecosystems is a challenge to landscape designers and forest ecologists. With a systematic grid-sample-plot investigation and landscape-attributes extraction of SPOT-5 imagery in a tropical forest region in Hainan Island, China, we developed a landscape classification system of land cover, successional stages, and dominant plant functional groups in species-rich forest ecosystems. We classified the study landscape into eight land cover types, four successional stages, and six functional patch types, with accuracies at ≥ 78%. The patches dominated by the pioneer functional groups were mainly distributed in areas of early recovery stages on sunny slopes at elevations < 850 m, while the climax functional groups had more occupancies in the late recovery stages on shaded slopes at elevations > 850 m. The slope gradient had no significant influence on the patch distribution patterns in the study region. Our results show that species-rich forest landscapes can be classified into patch types of different dominant functional groups and successional stages through remote sensing in conjunction with ground survey and GIS.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it