{"id":"W4406492050","doi":"10.1145/3712709","title":"Exploring Large Language Models for Personalized Recipe Generation and Weight-Loss Management","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Computing for Healthcare","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Dalhousie University; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Recipe; Computer science; Weight loss; Medicine; History; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005101602,0.0008537548,0.0004825578,0.0007983655,0.0006387478,0.002687819,0.001235808,0.001135514,0.00448651],"category_scores_gemma":[0.02447969,0.0006014571,0.001599303,0.0006028865,0.0007653626,0.003585645,0.001679588,0.001927349,0.0008782716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001562538,"about_ca_system_score_gemma":0.001626082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01065643,"about_ca_topic_score_gemma":0.01782192,"domain_scores_codex":[0.9970048,0.002176032,0.0001077957,0.0003665801,0.0002485999,0.00009619386],"domain_scores_gemma":[0.9719068,0.02563058,0.0006689599,0.0008658429,0.0006321878,0.0002956837],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002200247,0.00201843,0.03359165,0.001595658,0.0005995616,0.001761368,0.01464507,0.4934303,0.01645197,0.1231569,0.01172003,0.2988288],"study_design_scores_gemma":[0.00008304619,0.0001640507,0.0008180399,0.00005523223,0.00006586771,0.0001160052,0.0006164304,0.9596026,0.001286792,0.03122786,0.005927786,0.00003626048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1837298,0.001004072,0.7966115,0.003476197,0.0001283421,0.0006018275,0.001428183,0.004218189,0.008802007],"genre_scores_gemma":[0.6727005,0.0003660687,0.3210566,0.0005221486,0.00005585898,0.00061045,0.001490472,0.0002631085,0.002934752],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01065643,"threshold_uncertainty_score":0.02698022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1765687461847608,"score_gpt":0.4413326040818012,"score_spread":0.2647638578970404,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}