{"id":"W2482038647","doi":"10.1111/cogs.12391","title":"Arranging Objects in Space: Measuring Task‐Relevant Organizational Behaviors During Goal Pursuit","year":2016,"lang":"en","type":"article","venue":"Cognitive Science","topic":"Action Observation and Synchronization","field":"Psychology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Killam Trusts","keywords":"Task (project management); Computer science; Object (grammar); Space (punctuation); Cognitive psychology; Variety (cybernetics); Human–computer interaction; Psychology; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0004316829,0.0002645976,0.0002002738,0.0004393539,0.0002429837,0.0004663579,0.00020826,0.0003573762,0.0006117221],"category_scores_gemma":[0.001950002,0.0002352549,0.0001092619,0.0003115251,0.000334089,0.0003932647,0.0005760344,0.0003519284,0.0001326496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001309683,"about_ca_system_score_gemma":0.0001994205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005785258,"about_ca_topic_score_gemma":0.001167059,"domain_scores_codex":[0.9997442,0.00006191943,0.00001835857,0.00008682936,0.00005496346,0.00003376307],"domain_scores_gemma":[0.9988527,0.0003366933,0.0004359215,0.00009541029,0.00006108146,0.0002181164],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00150208,0.0009535422,0.1747566,0.0001850908,0.0001045898,0.00007072824,0.003425649,0.001280815,0.7760693,0.0004493585,0.0001650139,0.04103731],"study_design_scores_gemma":[0.00003776611,0.001334913,0.9743154,0.000008596396,0.00003398488,0.00009389353,0.0004901492,0.002929862,0.01989804,0.0005325502,0.0002999751,0.00002488783],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973936,0.00003061765,0.002164557,0.000005471264,0.000001170302,0.00002149952,0.00003180518,0.00001079513,0.0003403506],"genre_scores_gemma":[0.9925201,0.00005567214,0.006935854,0.00001246554,0.0000037283,0.00007849112,0.0001270281,0.00001026362,0.0002563797],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006117221,"threshold_uncertainty_score":0.002282977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02434897596264797,"score_gpt":0.2952831556186123,"score_spread":0.2709341796559644,"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."}}