{"id":"W4245479670","doi":"10.17722/ijme.v9i3.374","title":"Break-Even Analysis as a powerful tool in Decision-Making","year":2017,"lang":"en","type":"article","venue":"International Journal of Management Excellence","topic":"Decision-Making and Behavioral Economics","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.01344305,0.001588618,0.001701432,0.005288837,0.002091532,0.004960423,0.00187042,0.001741726,0.01891511],"category_scores_gemma":[0.05333804,0.0007444368,0.001213527,0.003028776,0.004151372,0.01023187,0.004794459,0.004995997,0.001525916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001321623,"about_ca_system_score_gemma":0.001921497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001036169,"about_ca_topic_score_gemma":0.001009516,"domain_scores_codex":[0.9933742,0.004199156,0.0003018175,0.0005106295,0.001342774,0.0002714352],"domain_scores_gemma":[0.9366719,0.05419071,0.002828351,0.003583858,0.001510743,0.001214428],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004449162,0.0001526614,0.001767751,0.0003167529,0.0001560571,0.0002205371,0.0009058046,0.02668436,0.001819369,0.8259236,0.005100506,0.1365076],"study_design_scores_gemma":[0.00002639915,0.00005391866,0.0002827944,0.00005455434,0.00002586552,0.00004627093,0.0001700492,0.08012357,0.0007081684,0.9138365,0.004644393,0.00002745342],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01142495,0.0004564944,0.9733525,0.001292316,0.0001874703,0.00009552608,0.0001659114,0.0006664685,0.01235831],"genre_scores_gemma":[0.4090416,0.0005760568,0.5836245,0.0003577234,0.0002616096,0.000488746,0.0002054669,0.000529539,0.004914716],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01891511,"threshold_uncertainty_score":0.07109451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05264939498581956,"score_gpt":0.4338162750717487,"score_spread":0.3811668800859292,"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."}}