{"id":"W7055376395","doi":"","title":"Characterization and Management of Food Loss and Waste in North America","year":2018,"lang":"en","type":"report","venue":"Issue Lab (Candid)","topic":"Advanced Frequency and Time Standards","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Food waste; Scope (computer science); Government (linguistics); White paper; Food systems; Food industry; Commission; Supply chain; Food safety","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006800211,0.0002116638,0.0002407677,0.003031237,0.002197971,0.002039318,0.00055257,0.0003550275,0.001084327],"category_scores_gemma":[0.001139483,0.0001622596,0.000177769,0.00589063,0.001120042,0.0008111166,0.001015721,0.0002820389,0.0001343796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005256161,"about_ca_system_score_gemma":0.004956664,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4534381,"about_ca_topic_score_gemma":0.6863699,"domain_scores_codex":[0.9992617,0.0001197762,0.00004150133,0.0001387181,0.0002681768,0.0001702023],"domain_scores_gemma":[0.9987779,0.0001599037,0.000342584,0.00004618951,0.0006112802,0.00006216218],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002559063,0.0001883993,0.8162615,0.0004714655,0.00007616596,0.001583731,0.04168634,0.0007933035,0.01150704,0.001848973,0.00384725,0.1214798],"study_design_scores_gemma":[0.000002573313,0.00007128157,0.9165577,0.0001310114,0.00002347581,0.0003024442,0.05537569,0.0004199322,0.002494156,0.0003536346,0.02425057,0.00001756264],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9824337,0.001645947,0.0006911706,0.0004770892,0.000009786682,0.0001174407,0.001094091,0.000015482,0.01351521],"genre_scores_gemma":[0.9885867,0.00337395,0.001804072,0.0003241676,0.000009317441,0.000122265,0.001586808,0.00001456685,0.00417815],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.546562,"threshold_uncertainty_score":0.9015976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0100717917762912,"score_gpt":0.2603771498131267,"score_spread":0.2503053580368355,"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."}}