{"id":"W4398458420","doi":"10.7910/dvn/ltqfm7","title":"Replication Data for: Improving Estimates of Transitions from Satellite Data: A Hidden Markov Model Approach","year":2022,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Replication (statistics); Computer science; Satellite; Markov model; Markov chain; Hidden Markov model; Artificial intelligence; Statistics; Mathematics; Machine learning; Physics; Astronomy","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":[0.006930202,0.001416679,0.001281261,0.002761759,0.0008016396,0.001892242,0.004452504,0.002240874,0.02481504],"category_scores_gemma":[0.03476189,0.000788917,0.002091072,0.004949864,0.0004237414,0.001332827,0.002461582,0.002156978,0.02050379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001426994,"about_ca_system_score_gemma":0.002607313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03792064,"about_ca_topic_score_gemma":0.06244199,"domain_scores_codex":[0.9974172,0.001166608,0.000320004,0.0004866737,0.0004468819,0.0001626529],"domain_scores_gemma":[0.9891511,0.003961954,0.0009133554,0.003832329,0.001666943,0.0004742675],"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":[0.0003350498,0.00006195706,0.006662416,0.001058023,0.0002783006,0.00005960506,0.00007008432,0.001833459,0.0001473284,0.002279979,0.9764363,0.01077759],"study_design_scores_gemma":[0.002855912,0.00009456167,0.03574353,0.001016969,0.0003700065,0.0002640348,0.000224775,0.01142841,0.00105952,0.01492827,0.9318483,0.0001656854],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001056594,0.000294102,0.001278975,0.0004386268,0.00008694577,0.00006149968,0.9954039,0.0007961449,0.0005832775],"genre_scores_gemma":[0.004258811,0.000117139,0.004488189,0.0001472568,0.00002757527,0.0003620956,0.9898886,0.0001483535,0.0005620441],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03792064,"threshold_uncertainty_score":0.08301455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06768329912757924,"score_gpt":0.324892770305761,"score_spread":0.2572094711781817,"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."}}