{"id":"W6892070315","doi":"10.5061/dryad.5hqbzkh9t","title":"Multi-generation selective landscapes and sub-lethal injuries in stickleback","year":2023,"lang":"en","type":"dataset","venue":"DRYAD","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Stickleback; Predation; Trait; Population; Incidence (geometry); Cohort; Stabilizing selection; Predator","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0007019473,0.0004497195,0.0006155924,0.001839,0.0007143442,0.0009578071,0.001368568,0.0005357902,0.01080673],"category_scores_gemma":[0.001652268,0.0003872605,0.0004939015,0.002732649,0.0003369257,0.0003422564,0.0009690888,0.0005244988,0.003737156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001157689,"about_ca_system_score_gemma":0.001040013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2227555,"about_ca_topic_score_gemma":0.4112057,"domain_scores_codex":[0.9996504,0.00005263036,0.00003570459,0.000142288,0.00005805329,0.00006097367],"domain_scores_gemma":[0.9991991,0.0001678026,0.0002527999,0.0001875613,0.0001235685,0.00006924167],"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":[0.0008648547,0.0001694748,0.2737454,0.001972782,0.0005440081,0.0004768698,0.0008457059,0.005681418,0.002519432,0.002742718,0.6905941,0.01984321],"study_design_scores_gemma":[0.0004397026,0.00004801146,0.5881322,0.0003262255,0.0001575975,0.0002258752,0.0006438104,0.003475121,0.0008348929,0.0008290097,0.4047964,0.00009120842],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.02751832,0.000214228,0.0002971305,0.00009536712,0.00002029999,0.00001608421,0.9704927,0.0001731594,0.001172706],"genre_scores_gemma":[0.02846704,0.0001150385,0.001092919,0.00005704016,0.000007453575,0.0001317033,0.968343,0.00007234743,0.001713543],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2227555,"threshold_uncertainty_score":0.4429178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02718456482917439,"score_gpt":0.296993869896348,"score_spread":0.2698093050671735,"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."}}