{"id":"W2078712083","doi":"10.1080/07373930008917761","title":"ANALYSIS OF RECENT PATENT LITERATURE ON DRYING AND DRYERS","year":2000,"lang":"en","type":"article","venue":"Drying Technology","topic":"Research, Science, and Academia","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Patent analysis; Subject (documents); Environmental science; Computer science; Library science; Data science","routes":{"ca_aff":true,"ca_fund":false,"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":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.005898812,0.0004379521,0.001030223,0.08504449,0.0006001101,0.001992243,0.0007343325,0.0006193884,0.01003274],"category_scores_gemma":[0.02601,0.000163535,0.001080464,0.0624376,0.000478401,0.002169158,0.0005320216,0.0004019787,0.001305277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001413084,"about_ca_system_score_gemma":0.002495744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002270635,"about_ca_topic_score_gemma":0.003820059,"domain_scores_codex":[0.9962261,0.0005626474,0.0008348866,0.0003643799,0.001832858,0.0001792543],"domain_scores_gemma":[0.9447861,0.03314199,0.008614532,0.0007499472,0.01191,0.0007974684],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009891366,0.0002331075,0.01863839,0.05650863,0.0007568497,0.001719545,0.001232706,0.001371849,0.01262444,0.006661253,0.03394903,0.865315],"study_design_scores_gemma":[0.0001274881,0.001669627,0.282032,0.02460388,0.004321982,0.003439835,0.00290094,0.002296215,0.01381095,0.003927336,0.6607401,0.0001296644],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.2379512,0.6554232,0.00552395,0.005092392,0.0009904549,0.0007826845,0.04347256,0.0002217228,0.05054187],"genre_scores_gemma":[0.3951911,0.5345147,0.01276364,0.001041133,0.00198453,0.0006570244,0.04493843,0.00005461683,0.00885482],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9149555,"threshold_uncertainty_score":0.0335629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1366806827486899,"score_gpt":0.4031952796658346,"score_spread":0.2665145969171447,"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."}}