{"id":"W6958255942","doi":"10.6084/m9.figshare.10261016.v5","title":"The new kid on the block: Immigrant males win big whereas females pay fitness cost after dispersal - data, meta-data, and code","year":2020,"lang":"en","type":"dataset","venue":"Figshare","topic":"Agricultural pest management studies","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Immigration; Biological dispersal; Code (set theory); Natural experiment; Offspring; Code of practice","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001442897,0.001057139,0.001227505,0.003478614,0.0006770987,0.001839295,0.00284111,0.001645179,0.05739558],"category_scores_gemma":[0.008644301,0.0007589206,0.0015283,0.006013292,0.0003946642,0.001156209,0.001873403,0.001154053,0.02508004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001985205,"about_ca_system_score_gemma":0.003122453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1262311,"about_ca_topic_score_gemma":0.2268528,"domain_scores_codex":[0.9991342,0.0001537156,0.0001512456,0.0002394511,0.0001721821,0.0001490966],"domain_scores_gemma":[0.9962057,0.00146249,0.0005891263,0.000638476,0.0007552696,0.000348863],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0001180985,0.00001347709,0.004795191,0.002812709,0.0002496848,0.00002883117,0.00004824786,0.0005136667,0.0001276487,0.0005425458,0.9884991,0.00225083],"study_design_scores_gemma":[0.001328662,0.00003401421,0.05207997,0.001602556,0.0004675623,0.00009881641,0.0002285459,0.001091803,0.0003721527,0.001849272,0.9407528,0.00009388525],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001603484,0.00007065487,0.00002320106,0.00005011361,0.0000124134,0.000006050354,0.999297,0.00009065925,0.000289487],"genre_scores_gemma":[0.002427737,0.0001196907,0.0003666073,0.00009029939,0.00000756045,0.0001116945,0.9961165,0.0001129492,0.000647039],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1262311,"threshold_uncertainty_score":0.2509928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1466985651523816,"score_gpt":0.2729202725433235,"score_spread":0.126221707390942,"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."}}