{"id":"W6927479887","doi":"10.34675/l21812","title":"Exclusive patent, know-how and technology license to use, make, offer, sell and import products made using heat generative engines that use water as the working fluid and the lubricant, for use in the field of electrical power generation that uses biomass fuels as a source and electrical power generation for the U.S. Military establishment, excluding motive power or generation units in the automotive and truck industries, with amendments to change territory to be worldwide for U.S. military use, nonexclusive for biomass generators 1 MGW and above in the USA, Canada, the Caribbean and Israel, and exclusive for all generators and engines for Israeli military applications, and to add a license for Europe.","year":2019,"lang":"en","type":"article","venue":"RoyaltyStat Library","topic":"Forest Biomass Utilization and Management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"License; Work (physics); Licensee; Order (exchange)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006674316,0.0005525019,0.0005055512,0.00044274,0.000669313,0.0004374201,0.0002042034,0.0001839912,4.338768e-7],"category_scores_gemma":[0.0003985695,0.0002816805,0.00003211548,0.0006970013,0.0002097108,0.0007368351,0.0002390659,0.0001631419,1.009002e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006599452,"about_ca_system_score_gemma":0.00010613,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003053025,"about_ca_topic_score_gemma":0.03112567,"domain_scores_codex":[0.9975546,0.0002560357,0.0004743437,0.0008934029,0.0002239683,0.0005976215],"domain_scores_gemma":[0.996668,0.002507008,0.000100125,0.0003656533,0.0002196491,0.0001395165],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.02568492,0.0008251161,0.3472219,0.002797969,0.003575321,0.00005000749,0.2264986,0.007523684,0.2464684,0.01647278,0.104183,0.01869819],"study_design_scores_gemma":[0.02522464,0.01138195,0.09819478,0.0006710347,0.001921319,0.0003609179,0.06194134,0.4552539,0.1263386,0.0004181957,0.214217,0.004076326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9529048,0.006103376,0.008424302,0.011076,0.0001041882,0.02056327,0.0008030279,0.00002072786,3.769859e-7],"genre_scores_gemma":[0.9777826,0.001358082,0.005617574,0.008111921,0.0001625063,0.006552476,0.0002578686,0.00009852961,0.00005842998],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4477302,"threshold_uncertainty_score":0.9999635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04994593972455359,"score_gpt":0.2426735608876488,"score_spread":0.1927276211630952,"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."}}