{"id":"W4396670442","doi":"10.1029/2023wr036099","title":"A Larval “Recruitment Kernel” to Predict Hatching Locations and Quantify Recruitment Patterns","year":2024,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Ministry of Natural Resources and Forestry; Royal Military College of Canada; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Nature Conservancy; University of Toledo; Ontario Ministry of Natural Resources and Forestry","keywords":"Biological dispersal; Hatching; Kernel (algebra); Larva; Population; Biology; Statistics; Ecology; Mathematics; Demography","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":[0.0007830156,0.0003103133,0.0002419416,0.0005927215,0.0001726937,0.0003838207,0.0004211885,0.000284681,0.0007899687],"category_scores_gemma":[0.001751129,0.0002173912,0.0003132829,0.0002572926,0.0002098283,0.0005466702,0.0003908467,0.0002687003,0.0002615624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000618551,"about_ca_system_score_gemma":0.0005410709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01057766,"about_ca_topic_score_gemma":0.009824414,"domain_scores_codex":[0.9998934,0.0000268696,0.000009792186,0.00003625056,0.00001848456,0.00001513775],"domain_scores_gemma":[0.9991965,0.0003379245,0.0002086551,0.00006643588,0.0001315792,0.00005888465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001851121,0.0001310665,0.3567004,0.0000525162,0.00009795349,0.00007271057,0.0000827343,0.5871247,0.01237412,0.002257356,0.0006042424,0.04031692],"study_design_scores_gemma":[0.000004825521,0.00002569568,0.02202979,0.000004958391,0.000005896023,0.00001576475,0.000008340044,0.9766214,0.0008412553,0.0003355849,0.0001003207,0.000006086874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7563112,0.00007023376,0.2417331,0.00007505822,0.000008442616,0.00003198476,0.0003005202,0.0003868258,0.001082587],"genre_scores_gemma":[0.9858385,0.00001719307,0.01365862,0.00000704259,0.000001650901,0.00001466495,0.00009354234,0.00001211905,0.0003567275],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01057766,"threshold_uncertainty_score":0.02103215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1353163338593479,"score_gpt":0.3749871472468599,"score_spread":0.2396708133875119,"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."}}