Food and environment II: the quest for a sustainable future.
Bibliographic record
Abstract
Contents Section 1: Impact of food production and food processing on the environment The nutritional footprint: an innovative management approach for the food sector Impacts of agricultural activities in remaining forest: Campinas/SP, Brazil Biorefinery meets biofermentation to produce food additives Food versus fuel: the case of the Makeni community in Sierra Leone Soil erodibility assessment in a pasture and forest remnant using the Inderbitzen device Microbial composition of the abattoir environment and its health implications on the quality of fresh cow meat sold in Akure, Ondo State, Nigeria Section 2: Policies and regulations The United States Department of Agriculture policies to improve school food quality while protecting the environment Sustainability reports from the food industry: case studies from Europe and Latin America The importance of food retail stores in identifying food deserts in urban settings Section 3: Food production and climate Climate change adaptation and food production in Canada: some research challenges Characterization of climate and crop productivity using DSSAT for SW Uttarakhand, India Section 4: Contamination of food An HACCP approach integrating quantitative microbial risk assessment and shelf life prediction Comparative analysis of lead and cadmium levels in various brands of canned and processed meat products in Lebanon A preventive approach to risk management in global fruit and vegetable supply chains Section 5: Temperature control and traceability Modelling heat transfer-controlled cooling and freezing times: a comparison between computational values and experimental results The contradiction between production, marketing and consumption of organic food: supply of organic products in the city of Barcelona Section 6: Pesticides and nutrients Phosphorus conventional use, reduction potential, and possibility of self-sufficiency during food and feed production in Japan A review of pesticide residue levels and their related health exposure risks Section 7: Food characterisation Antinutrient content, vitamin constituents and antioxidant properties in some value-added Nigerian traditional snacks Nutritional and medicinal value of camel (Camelus dromedarius) milk Preliminary investigation on use of mushroom to replace beef and turkey for pepper soup Wound healing effect and flavonoids contents of three selected Byrsonima species
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".