Understanding Land Use, Land Cover and Woodland-Based Ecosystem Services Change, Mabalane, Mozambique
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
Charcoal production constitutes a key ecosystem service in Mozambique, with an estimated market value of US$400 million a year. Due to the central role the charcoal industry plays in local livelihoods, availability of suitable wood for charcoal production has decreased because of changes in land use and land cover (LULC). This paper applied a probabilistic modelling approach combining Bayesian Belief Networks (BBNs), Geographic Information Systems, Remote Sensing data, field data, and expertise from different stakeholders to understand how changes in LULC affect woodland-based ecosystem services (ES) in the Mabalane landscape, southern Mozambique. Three scenarios of policy interventions were tested: Large private; Small holder and Balanced. A BBNs was used to explore the influence of these scenarios from 2014 to 2035 on the resulting LULC. This research facilitated stakeholder engagement and improved the understanding of the interaction between LULC changes and woodland-based ES. The results highlighted the importance and spatial distribution of woodland-based ES to the local communities and that availability of suitable wood for ES will decrease under the first scenario.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it