{"id":"W6921408205","doi":"10.7910/dvn/4d4xpt","title":"R code and data from: Preserving identity in capture-mark-recapture studies: Increasing the accuracy of minimum number alive (MNA) estimates by incorporating inter-census trapping efficiency variation","year":2021,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Context (archaeology); Row; Code (set theory); Matrix (chemical analysis); Position (finance); Identity (music); Enumeration; Identity matrix; Scale (ratio)","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":[0.00467861,0.002570811,0.002302275,0.0032884,0.001099822,0.003255987,0.003932827,0.001762868,0.1334392],"category_scores_gemma":[0.0265551,0.001390359,0.002175122,0.004738257,0.000833303,0.001884594,0.002793194,0.002609646,0.1384387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001794865,"about_ca_system_score_gemma":0.003184392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01529718,"about_ca_topic_score_gemma":0.02955094,"domain_scores_codex":[0.9971615,0.000649782,0.0003808,0.0009815833,0.000613556,0.0002127574],"domain_scores_gemma":[0.9903671,0.005062821,0.0006560055,0.002165261,0.001439285,0.0003096611],"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":[0.00006136839,0.00001495753,0.00116819,0.0009159312,0.0000892617,0.00002167306,0.00003176148,0.0008173413,0.0001771781,0.0009223041,0.9927636,0.003016439],"study_design_scores_gemma":[0.000596032,0.00003489314,0.005200347,0.0005906848,0.0001421191,0.0001289055,0.00005971274,0.001630634,0.0009405635,0.00662026,0.9839628,0.00009294274],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001813304,0.00006389362,0.001459308,0.0001178648,0.00003852303,0.00005900819,0.9951479,0.00221093,0.000721289],"genre_scores_gemma":[0.001070403,0.00009467972,0.007318853,0.0002181474,0.00001611617,0.001050786,0.9868428,0.002097126,0.001290992],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1334392,"threshold_uncertainty_score":0.4463985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04887193002963797,"score_gpt":0.339072022959179,"score_spread":0.2902000929295411,"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."}}