{"id":"W2099102163","doi":"10.4028/www.scientific.net/amm.462-463.1081","title":"Optimisation to ANN Inputs in Automated Property Valuation Model with Encog 3 and winGamma","year":2013,"lang":"en","type":"article","venue":"Applied Mechanics and Materials","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Victoria University; University of Victoria","keywords":"Valuation (finance); Artificial neural network; Property (philosophy); Computer science; Revenue; Artificial intelligence; Engineering; Operations research; Finance; Economics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0005381028,0.0005799704,0.0005067007,0.0003531395,0.0002767549,0.0006011288,0.0005886371,0.0008118222,0.003516601],"category_scores_gemma":[0.001249362,0.0004145208,0.0007262384,0.0002811792,0.0002611154,0.0004113235,0.0004387868,0.0007774345,0.0005323427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005752778,"about_ca_system_score_gemma":0.0006603008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008408126,"about_ca_topic_score_gemma":0.008889486,"domain_scores_codex":[0.9998305,0.00004407793,0.00001526061,0.00003361517,0.00004883027,0.00002764049],"domain_scores_gemma":[0.9995897,0.0002805951,0.00003283965,0.00002353273,0.00006593112,0.000007384748],"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":[0.00002591525,0.00002120223,0.000480111,0.00002737243,0.00001210883,0.00004399027,0.00001654399,0.9806543,0.001457543,0.0007816973,0.000224152,0.01625513],"study_design_scores_gemma":[0.000002515566,0.000007855505,0.0001403284,0.000003415266,0.000002746733,0.00000516074,0.000001876485,0.9986508,0.0006653698,0.0002503239,0.0002674873,0.000002117382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1347142,0.0002234716,0.8484737,0.0001740857,0.00005663902,0.0001499112,0.0003642725,0.002738524,0.01310508],"genre_scores_gemma":[0.7786721,0.0001551158,0.2123278,0.00008235293,0.00001310539,0.0004289997,0.0003751177,0.0001797646,0.007765744],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008408126,"threshold_uncertainty_score":0.01671839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08842737501765188,"score_gpt":0.3400543875631575,"score_spread":0.2516270125455057,"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."}}