{"id":"W1601855650","doi":"","title":"ORGANIZATIONAL ADAPTATION AND RESILIENCE TO EXTREME WEATHER EVENTS","year":2008,"lang":"en","type":"article","venue":"Queensland's institutional digital repository (The University of Queensland)","topic":"Complex Systems and Decision Making","field":"Decision Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Extreme weather; Climate change; Context (archaeology); Psychological resilience; Adaptation (eye); Environmental resource management; Resilience (materials science); Business; Environmental planning; Political science; Geography; Environmental science; Ecology; Psychology","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":[],"consensus_categories":[],"category_scores_codex":[0.0003496369,0.0001630346,0.0002712639,0.000241121,0.001078404,0.00009711379,0.0005752401,0.0000748935,0.0001007439],"category_scores_gemma":[0.0007427002,0.0001214472,0.0001089574,0.0006150958,0.0004414754,0.0006099385,0.0002611618,0.0001085418,0.0001993749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007475653,"about_ca_system_score_gemma":0.0002802002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002207087,"about_ca_topic_score_gemma":0.00007513203,"domain_scores_codex":[0.997292,0.0001123699,0.0004551707,0.0004765411,0.001479688,0.0001842488],"domain_scores_gemma":[0.9978749,0.0006312378,0.0002512976,0.0004080377,0.0006633814,0.0001711625],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008340482,0.0002800416,0.8438349,0.00001782098,0.0001235412,0.0004468014,0.008137509,0.01660073,0.001027248,0.09341514,0.02709472,0.00818752],"study_design_scores_gemma":[0.001037819,0.000173422,0.8827409,0.0001253231,0.00002152127,0.001016347,0.002478902,0.005002148,0.00007201425,0.02211302,0.08480288,0.0004157249],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8701697,0.0001908434,0.08575444,0.0004139366,0.000384128,0.0003213648,0.00006345658,0.00004928317,0.04265283],"genre_scores_gemma":[0.9869443,0.000006137479,0.0009606706,0.0000609828,0.00009037962,4.371457e-7,0.000005628044,0.000006904414,0.01192458],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1167746,"threshold_uncertainty_score":0.8294324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07569695165059717,"score_gpt":0.2666326117179639,"score_spread":0.1909356600673667,"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."}}