Effects of climate variability on the harvesting and preservation of Mopani worms
Bibliographic record
Abstract
The effects of climate variability on the harvesting and preservation of Mopani \nworms are addressed in this review. Human consumption of insects has occurred \nfor millennia and has recently received increased attention in the literature. \nGiven the intimate link between the larvae of lmbrasiabelina (i.e. the \nMopani worm), Mopani woodlands and rainfall, climate change and weather \nvariability will likely have negative effects on Mopani worm availability, harvesting, \npreservation and nutritional status of global communities including sub-Saharan- \nAfrican (hence forth SSA) countries. The literature review presented in \nthis article covered the period between 1982 and 2015 relating to the searchfor \nalternative nutrient food sources which was veiy prominent during the period of \nvariable climatic conditions. Both qualitative and quantitative literature was read. \nIntensive data mining of reports and publications using Google search engine \nwas carried out, using the words Mopani worms, harvesting and preservation as \nkey search words. Climate variability effect on the Mopani woodlands, Mopani \nworms developmental stages, harvesting, preservation, economic development \nand nutrition are discussed. The recommendations made are that the negative \neffects of climate variability on Mopani woodlands and Mopani worms need \nto be mitigated to ensure food security and sustained economic development.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".