{"id":"W1502171503","doi":"10.2139/ssrn.940477","title":"Determinants of Survival of De Novo Entrants in Clusters and Dispersal","year":2006,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Firm Innovation and Growth","field":"Economics, Econometrics and Finance","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Biological dispersal; Biology; Ecology; Geography; Demography; Sociology; Population","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001430757,0.0001532648,0.0004174236,0.001006164,0.000739345,0.001899857,0.0006314354,0.001030099,0.009164992],"category_scores_gemma":[0.01035646,0.000201437,0.0004514068,0.0009267018,0.0008889481,0.001407467,0.001291679,0.001101846,0.0007050974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007379315,"about_ca_system_score_gemma":0.0005801932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01055062,"about_ca_topic_score_gemma":0.01538477,"domain_scores_codex":[0.999531,0.00008250272,0.00004211588,0.00009732271,0.00004070757,0.0002063484],"domain_scores_gemma":[0.9813234,0.006586671,0.005712556,0.000810579,0.001450394,0.004116523],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003591149,0.0001179372,0.9801614,0.00003060397,0.0001240534,0.0004833558,0.0006399688,0.005342328,0.001030057,0.003185714,0.0006527379,0.007872816],"study_design_scores_gemma":[0.00004339659,0.0002040099,0.9849015,0.00003097365,0.00009089887,0.0004319257,0.003520502,0.005736065,0.0004547679,0.003632453,0.0009255694,0.0000279619],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985064,0.0001682449,0.0003098813,0.0002221575,0.000005279619,0.000007216467,0.0001093422,0.000007804811,0.0006637738],"genre_scores_gemma":[0.9991228,0.00005642954,0.00003957582,0.0000111239,0.000007135393,0.000001838967,0.00007135928,0.000001362312,0.0006884554],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01055062,"threshold_uncertainty_score":0.03065991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00981818043320462,"score_gpt":0.2132219260176707,"score_spread":0.2034037455844661,"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."}}