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Record W7048331300

La responsabilita del produttore alimentare per danni da obesita

2021· book· it· W7048331300 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueUniversità Politecnica delle Marche (Università Politecnica delle Marche) · 2021
Typebook
Languageit
FieldEngineering
TopicPhotocathodes and Microchannel Plates
Canadian institutionsnot available
Fundersnot available
KeywordsUnpaid workQuarter (Canadian coin)Product (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

La diffusione dell’obesità nei paesi occidentali, con conseguente aumento dei rischi per la salute delle persone, ha posto il problema dell’individuazione di strumenti atti a contrastare il fenomeno. Negli Stati Uniti, l’esperienza delle azioni intentate nei confronti delle multinazionali del cibo ha indotto i produttori ad immettere sul mercato alimenti piú salutari e ad educare i consumatori. Il trapianto del modello della fast food litigation in Italia deve, tuttavia, fare i conti con le peculiarità del nostro sistema di responsabilità civile. Appurato che la commercializzazione di alimenti nocivi è riconducibile al paradigma della responsabilità del produttore, occorre dissipare i dubbi circa la prova del difetto e del nesso eziologico, nonché indagare la rilevanza dell’assunzione del rischio da parte del consumatore come causa di esclusione della responsabilità. Valutando l’opportunità di rimettere la regolazione dell’obesità alla legislazione o al contenzioso, emerge la natura polifunzionale della responsabilità civile, idonea a realizzare non soltanto funzioni compensative, ma anche punitive, deterrenti ed educative, incentivando l’adozione di misure di sicurezza e qualità e orientando i consumatori verso scelte piú consapevoli.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies, Open science, Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.338
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0040.006
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0040.003
Science and technology studies0.0020.003
Scholarly communication0.0010.002
Open science0.0070.009
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0080.002

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.

Opus teacher head0.019
GPT teacher head0.231
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it