{"id":"W4403351276","doi":"10.1111/ele.14535","title":"Predicting and Prioritising Community Assembly: Learning Outcomes via Experiments","year":2024,"lang":"en","type":"article","venue":"Ecology Letters","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministerio de Ciencia, Innovación y Universidades; McGill University; National Science Foundation","keywords":"Abundance (ecology); Species richness; Ecology; Biodiversity; Mechanism (biology); Community; Community structure; Computer science; Environmental resource management; Machine learning; Habitat; Environmental science; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.01012984,0.001727485,0.0006763009,0.000403135,0.0005132143,0.001088043,0.00192897,0.002214284,0.001884544],"category_scores_gemma":[0.03105871,0.0005646109,0.0009848953,0.0003316152,0.001759612,0.003151421,0.001612305,0.003276053,0.0007400009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001171529,"about_ca_system_score_gemma":0.0008498629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002522991,"about_ca_topic_score_gemma":0.005055127,"domain_scores_codex":[0.9972953,0.001344251,0.0001332691,0.0007962334,0.0002192204,0.0002116669],"domain_scores_gemma":[0.9678972,0.02571099,0.001818546,0.002632838,0.0009058308,0.001034542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002586077,0.00342998,0.1041755,0.0006290696,0.0005755036,0.0002489679,0.0006111373,0.7546831,0.01409766,0.005080434,0.008617748,0.1052649],"study_design_scores_gemma":[0.0001813477,0.001136261,0.005044127,0.00004568845,0.00006588831,0.00004853516,0.00007263644,0.9748647,0.00781328,0.009616884,0.001065185,0.00004549397],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9035976,0.000483263,0.08802824,0.001231935,0.0001991353,0.0004608259,0.001110639,0.001252866,0.003635496],"genre_scores_gemma":[0.9418504,0.0001150976,0.05404788,0.0006087819,0.00005616754,0.0004513342,0.001438928,0.00008920609,0.001342289],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01012984,"threshold_uncertainty_score":0.05357236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02253730564533998,"score_gpt":0.2763274582661397,"score_spread":0.2537901526207997,"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."}}