Evaluating the impacts of nearly 30 years of conservation on grassland ecosystem using Landsat <scp>TM</scp> images
Bibliographic record
Abstract
Abstract When grassland degradation became a global issue because of overgrazing and other human activities, grassland managers implemented different management methods in an attempt to restore grassland ecosystems (e.g. conservation actions). Few studies have investigated the impacts of conservation actions (removing large grazers and conserving biodiversity) on grassland ecosystems, therefore, this study aims to evaluate the impacts of conservation actions and to measure the time lag of the significant influences of grassland conservations on mixed grasslands that were assessed by five different biophysical parameters (biodiversity, soil organic matter, fresh biomass, litter cover and green cover). Instead of measuring the biophysical parameters from field data and remotely sensed images, the methodology of this study focused on the difference in biophysical parameters between the ecological comparison sites (grazing sites and conserved sites), which enhance the impacts of grassland conservation on grasslands comparing to grazing management. The results show that: (i) biodiversity in conserved grasslands increased gradually in the first 3–5 years, decreased gradually over the next 4 or 5 years, and then stabilized after that; (ii) soil organic matter increased and reached its maximum value within 7–9 years of conservation and then remained at this level, while litter accumulated the maximum level one year later than soil organic matter; (iii) soil organic matter is the primary factor of biodiversity in the grasslands with low litter accumulation, while high‐density litter layers are the primary cause of decreases in biodiversity; (iv) fresh biomass decreased over a period of 7–10 years under conservation and remained nearly unchanged after that; (v) green vegetation fraction was also increased by conservation action in about 6–7 years. The result of this study provides the fundamental information for implementing and adjusting grassland management policies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".