Editorial: Impacts of Tropical Landscape Change on Human Diet and Local Food Systems
Bibliographic record
Abstract
The impacts of changing diets on land use and land cover has been an important area of research in recent years (Foley et al., 2011; Tilman and Clark, 2014; Fanzo and Davis, 2019; Willett et al., 2019). This special issue looks at the reverse side of this relationship – how land use change affects the diets of local communities living in landscapes where change is taking place. Clear links between forest cover and diet and nutritional outcomes have been shown (Johnson et al., 2013; Ickowitz et al., 2014; Rasolofoson et al., 2018; Fisher et al., 2019), while more recent work has started to disentangle the differential impacts of land use type, composition and configuration on diets and the consumption of specific food groups (Rasmussen et al., 2019; Gergel et al., 2020). This special issue brings together a collection of papers that examine the effects of land use and land use change on diet and nutritional outcomes in the tropics. It assembles papers from a wide range of disciplines, covering the links between forest conservation, deforestation, hydropower development, and changing patterns of agricultural production on diets and nutrition across a range of settings.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.020 | 0.014 |
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".