{"id":"W4391392314","doi":"10.22541/au.170668321.15899916/v1","title":"The fitness value of ecological information in a variable world","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Plant and animal studies","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Ecology; Variable (mathematics); Evolutionary ecology; Population; Per capita; Component (thermodynamics); Value (mathematics); Value of information; Environmental change; Information processing; Ecosystem; Computer science; Environmental resource management; Biology; Artificial intelligence; Economics; Psychology; Sociology; Climate change; Mathematics; Machine learning; Cognitive psychology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001866819,0.00042785,0.0006194084,0.001247854,0.0004701322,0.003172386,0.0006006809,0.001532905,0.00220163],"category_scores_gemma":[0.008771136,0.0001930388,0.0005334096,0.001072756,0.003774775,0.004603787,0.001406427,0.001634223,0.0002534375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00103312,"about_ca_system_score_gemma":0.0004109823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006773579,"about_ca_topic_score_gemma":0.0004576692,"domain_scores_codex":[0.9990985,0.0003889239,0.00003882789,0.0001835414,0.0001944763,0.00009564708],"domain_scores_gemma":[0.9928923,0.005760732,0.0005367094,0.0003967228,0.0002503568,0.0001632612],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004961123,0.00002563452,0.006007711,0.0003538791,0.000131886,0.000289445,0.00031959,0.01629356,0.003685517,0.9165215,0.001683041,0.05463861],"study_design_scores_gemma":[0.000006561996,0.00004133568,0.006199072,0.0000940006,0.00004177123,0.0002583716,0.0001025109,0.01264831,0.0006394961,0.9744511,0.005487427,0.00003011338],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4745745,0.03980299,0.3686492,0.02543495,0.000616037,0.00004281494,0.0007498111,0.0002063354,0.08992324],"genre_scores_gemma":[0.9717196,0.009463114,0.01522868,0.0007665023,0.0003591196,0.00003074458,0.0001544002,0.00004187987,0.002236037],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003172386,"threshold_uncertainty_score":0.009872794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03258805235768374,"score_gpt":0.2173146323956935,"score_spread":0.1847265800380098,"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."}}