{"id":"W6926516535","doi":"10.25318/3310072701-eng","title":"Most challenging obstacle expected by the business or organization over the next three months, fourth quarter of 2023","year":2023,"lang":"en","type":"dataset","venue":"Statistics Canada Dissemination","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Obstacle; Quarter (Canadian coin); Business model; Business development; Business cycle","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006305537,0.0003142661,0.0003216613,0.00007910441,0.0005278052,0.0003296478,0.001067354,0.0001226249,0.001521961],"category_scores_gemma":[0.004117456,0.0001945094,0.0000122061,0.00130951,0.0001768263,0.0001621661,0.0001855225,0.0002227651,0.00005617396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002223462,"about_ca_system_score_gemma":0.0006590312,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3091904,"about_ca_topic_score_gemma":0.6208857,"domain_scores_codex":[0.9971699,0.0002168485,0.0005793041,0.0004454666,0.001209705,0.0003787726],"domain_scores_gemma":[0.9964057,0.001306701,0.0007597453,0.0007282505,0.0007357337,0.00006393373],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001140624,0.00001742884,0.00001451589,0.0001643777,0.000009949204,0.00001286296,0.0001172369,0.0008016822,0.004212839,0.00008627153,0.9943318,0.000219608],"study_design_scores_gemma":[0.002128025,0.0005421576,0.2627784,0.002896636,0.001282632,0.0001206791,0.01053347,0.0585603,0.01090525,0.00154167,0.6430229,0.005687812],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002574988,0.000065418,0.007828733,0.0005692158,0.002078087,0.0004997196,0.986334,0.00004417397,0.000005702677],"genre_scores_gemma":[0.004290835,0.00008872586,0.0003782629,0.00009378348,0.0001747343,0.00006247283,0.9944277,0.00006050541,0.0004229359],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3513089,"threshold_uncertainty_score":0.9993908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0104308222459487,"score_gpt":0.2543009117645934,"score_spread":0.2438700895186447,"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."}}