Global distribution of forest classes and leaf biomass for use as alternative foods to minimize malnutrition
Bibliographic record
Abstract
Abstract Due to the ready availability of tree leaves in many geographies, the alternative food of leaf concentrate currently has the potential to alleviate hunger in over 800 million people. It is therefore potentially highly impactful to determine the edibility of leaf concentrates, which are in the same regions as the world's most undernourished populations. Unfortunately, the toxicity of leaf concentrate for most common tree leaf types has not been screened and the cost of doing so demands a prioritization. This preliminary study explores this potential solution to world hunger by finding the forest classes most likely to offer proximate access to the world's hungry, thus providing the basis for a prioritized list of leaf types to screen for toxicity. Specifically, this study describes a novel methodology for mapping available green leaf biomass and corresponding forest classes (e.g., tropical moist deciduous forest), and their spatial relationship to the global distribution of people who are underweight. These results will be useful for developing a targeted list of tree species to conduct leaf toxicity analysis on, in the interest of developing leaves as an alternative food source for both current malnutrition problems and global catastrophic scenarios.
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".