{"id":"W2288604827","doi":"10.1111/eva.12373","title":"Harvest‐induced evolution and effective population size","year":2016,"lang":"en","type":"article","venue":"Evolutionary Applications","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Academy of Finland","keywords":"Biology; Preharvest; Effective population size; Population; Population size; Ecology; Demography; Genetic variation; Botany","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00118087,0.000205623,0.0002416082,0.0003833556,0.0002866499,0.000571861,0.000520753,0.0004831232,0.001605431],"category_scores_gemma":[0.006812192,0.0001935979,0.0004350643,0.0003127088,0.0007386165,0.001060704,0.0005899196,0.0004976992,0.0001497052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008132829,"about_ca_system_score_gemma":0.000320272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001619569,"about_ca_topic_score_gemma":0.002026157,"domain_scores_codex":[0.9997271,0.0001099723,0.0000181562,0.00007452331,0.00003381553,0.00003643459],"domain_scores_gemma":[0.9980158,0.001289642,0.000272243,0.0002060839,0.0001059056,0.0001104264],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002288588,0.0001219738,0.2248251,0.0001740569,0.0005104457,0.0005395502,0.0004027563,0.6688796,0.02521494,0.0496335,0.001154406,0.02831479],"study_design_scores_gemma":[0.00008200204,0.0003489694,0.2061931,0.00004612873,0.0001560598,0.000521671,0.0002876496,0.7165331,0.003954624,0.06900662,0.002763667,0.0001064315],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9787239,0.0002147773,0.01652108,0.0002202448,0.00001620283,0.000009797037,0.0001641691,0.00004380313,0.004085981],"genre_scores_gemma":[0.9975471,0.0001090788,0.001742268,0.00003904893,0.000003672201,0.00001567787,0.00009556625,0.00001738854,0.000430106],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001619569,"threshold_uncertainty_score":0.006245136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007097088756658253,"score_gpt":0.2381969669822853,"score_spread":0.2310998782256271,"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."}}