Lo spreco alimentare in Italia e nel mondo. Quanto, cosa e perche – i rapporti dell’Osservatorio Waste Watcher International 2022-2023
Bibliographic record
Abstract
La riduzione dello spreco alimentare, soprattutto a seguito delle conseguenze economiche della pandemia e delle difficoltà d’approvvigionamento alimentare indotte dalla guerra in Ucraina, è diventato un obiettivo sempre più pressante non solo per le famiglie italiane. L’Osservatorio Waste Watcher International ha dunque allargato la sua analisi ad altri undici Paesi, per offrire uno sguardo approfondito e inedito nelle abitudini alimentari e di consumo dei cittadini di Italia, Francia, Spagna, Germania, Gran Bretagna, Russia, Usa, Canada, Brasile, Cina, Giappone e Sud Africa. L’indagine fa luce sulle cause dello spreco alimentare a livello domestico e individua le principali azioni, pubbliche e private, necessarie per contrastarlo e arginare il conseguente impatto ambientale, sociale ed economico.
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 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) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; both teacher heads agree on what is shown here.
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".