Valuing Ecosystem Services in Semi‐arid Rangelands through Stochastic Simulation
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
Abstract Ecosystem services and economic returns from semi‐arid rangelands are threatened by land degradation. Policies to improve ecosystem service delivery often fail to consider uncertainty in economic returns gained through different land uses and management practices. We apply an analytical framework using stochastic simulation to estimate the range of potential monetary outcomes of rangeland ecosystem services under different land uses, including consideration of the uncertainty and variability of model parameters. We assess monetary and non‐monetary dimensions, including those ecosystem services with uncertain and missing information, for communal rangelands, commercial ranches, game farms and Wildlife Management Areas in southern Kgalagadi District, Botswana. Public land uses (communal grazing areas and protected conservation land in Wildlife Management Areas) provide higher economic value than private land uses (commercial ranches and game farms), despite private land uses being more profitable in their returns from meat production. Communal rangelands and protected areas are important for a broader range of ecosystem services (cultural/spiritual services, recreation, firewood, construction material and wild food), which play a key role in sustaining the livelihoods of the largest share of society. The full range of ecosystem services should therefore be considered in economic assessments, while policies targeting sustainable land management should value and support their provision and utilisation. By forecasting the range of plausible ecosystem values of different rangeland land uses in monetary terms, our analysis provides policymakers with a tool to assess outcomes of land use and management decisions and policies. Copyright © 2016 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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