{"id":"W4386096151","doi":"10.22541/au.169278310.08644187/v1","title":"Temporal collections to study invasion biology","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Plant and animal studies","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Digitization; Pace; Quickening; Data science; Adaptation (eye); Natural (archaeology); Ecology; Biology; Geography; Computer science; Archaeology","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":[0.007181593,0.0009068443,0.0008215639,0.009011806,0.002426193,0.002838618,0.001938014,0.0009539857,0.01958857],"category_scores_gemma":[0.01352117,0.0007038795,0.0008345808,0.01249908,0.001849119,0.004296388,0.004945287,0.002201006,0.007095065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001167938,"about_ca_system_score_gemma":0.002597228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007242014,"about_ca_topic_score_gemma":0.02060596,"domain_scores_codex":[0.9960821,0.0009167922,0.0005474247,0.0008504037,0.001353363,0.000249996],"domain_scores_gemma":[0.982567,0.003034789,0.004844851,0.004383884,0.003919912,0.00124945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009667954,0.0002629933,0.1530757,0.008119996,0.001174025,0.002051107,0.02693694,0.001444483,0.03193121,0.08257352,0.1395938,0.5518694],"study_design_scores_gemma":[0.00004956191,0.000166947,0.1839019,0.001567222,0.0002599953,0.001941085,0.005998782,0.0003181008,0.00515979,0.01225613,0.7882515,0.0001290039],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2423603,0.08524504,0.2421728,0.005583364,0.007998335,0.004034296,0.1536794,0.002875865,0.2560507],"genre_scores_gemma":[0.3736418,0.04754988,0.3329483,0.003470051,0.004184095,0.007287854,0.1479691,0.00352191,0.07942696],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01958857,"threshold_uncertainty_score":0.06553024,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2247960367318262,"score_gpt":0.2924607849778019,"score_spread":0.06766474824597568,"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."}}