{"id":"W2053938886","doi":"10.1080/00420980701426640","title":"Intrametropolitan Employment Structure: Polycentricity, Scatteration, Dispersal and Chaos in Toronto, Montreal and Vancouver, 1996-2001","year":2007,"lang":"en","type":"article","venue":"Urban Studies","topic":"Regional Economics and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":91,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Institut National de la Recherche Scientifique","funders":"","keywords":"Metropolitan area; Polycentricity; Economic geography; Biological dispersal; Scale (ratio); Geography; Urban spatial structure; Regional science; Urban structure; Economics; Sociology; Corporate governance; Urban planning; Civil engineering; Cartography; Demography; Population; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002870273,0.0001729224,0.0004746217,0.0001693762,0.0001215533,0.00005284995,0.00007331163,0.00005830009,0.00006067176],"category_scores_gemma":[0.00006657265,0.0001737353,0.00005097886,0.0001148578,0.0001264636,0.0001943268,0.00009414521,0.00007335501,0.000003077622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005182815,"about_ca_system_score_gemma":0.000006537809,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04564825,"about_ca_topic_score_gemma":0.4720078,"domain_scores_codex":[0.9987146,0.000008726197,0.0005274942,0.0003936199,0.00003551688,0.0003200368],"domain_scores_gemma":[0.9994842,0.00008738787,0.0001692674,0.0001364355,0.00002508843,0.00009755776],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003086403,0.00005315563,0.9214687,0.00001553974,0.0002258947,0.000006249287,0.00140298,0.00000973976,0.000005569887,0.06935263,0.004051272,0.003377341],"study_design_scores_gemma":[0.001305605,0.0001572297,0.9557052,0.00001894188,0.00003525506,0.000004832482,0.003159938,0.002766397,0.00003370819,0.02921803,0.007067561,0.0005272566],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9524885,0.04321604,0.0003418765,0.0007538762,0.0003725427,0.0001719145,0.00008879977,0.00001397698,0.002552426],"genre_scores_gemma":[0.9948808,0.004031798,0.0002173546,0.0002868721,0.0001792688,0.000005453259,0.000007257,0.00001249989,0.0003786988],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4263595,"threshold_uncertainty_score":0.9607068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01833574259399694,"score_gpt":0.2417621771081454,"score_spread":0.2234264345141485,"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."}}