{"id":"W4253203007","doi":"10.26868/25222708.2019.210196","title":"A Generalized Inhomogeneous Markov Chain Occupancy Model For Open-Plan Offices Using Real Time Locating System Data","year":2020,"lang":"en","type":"article","venue":"Building Simulation Conference proceedings","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Occupancy; Markov chain; Computer science; Plan (archaeology); Data modeling; Real-time computing; Chain (unit); Machine learning; Engineering; Database","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009105389,0.0003640694,0.0005550587,0.0001731932,0.0003111586,0.0008566312,0.001532818,0.0001868372,0.00001605716],"category_scores_gemma":[0.0004250593,0.0004030051,0.00005286453,0.0004555705,0.00003395404,0.0009104996,0.0005868006,0.0002042391,0.00001502625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002346527,"about_ca_system_score_gemma":0.0001906202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001071975,"about_ca_topic_score_gemma":0.000004235198,"domain_scores_codex":[0.9973849,0.0000283992,0.0007152967,0.0008066553,0.0004659446,0.0005987961],"domain_scores_gemma":[0.9984659,0.0001915231,0.0002307789,0.000352014,0.0005204264,0.0002393741],"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":[0.0001436995,0.00001257153,0.001164914,0.001634858,0.00009298533,0.000002805732,0.001737781,0.8908248,0.1000945,0.001964433,0.000555506,0.001771146],"study_design_scores_gemma":[0.0008490265,0.00003331028,0.00002188326,0.0004719441,0.00004353544,0.000009732998,0.0002271342,0.9961696,0.001382139,0.00003956636,0.0003109088,0.0004412182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6197782,0.00007630761,0.376444,0.0000650564,0.0001320941,0.001754339,0.0001605286,0.0009065944,0.0006828812],"genre_scores_gemma":[0.9392062,0.000006224959,0.05992876,0.0000289641,0.0004334393,0.00009274793,0.0001149955,0.0001178969,0.00007073086],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3194281,"threshold_uncertainty_score":0.9998422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1736633123383023,"score_gpt":0.351553892856812,"score_spread":0.1778905805185098,"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."}}