{"id":"W4302008153","doi":"10.21203/rs.3.rs-2099527/v1","title":"The Maximum Entropy Principle to predict foragerspatial distributions: an alternate perspective formovement ecology","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Ecosystem dynamics and resilience","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Principle of maximum entropy; Inference; Entropy (arrow of time); Ecology; Computer science; Foraging; Ideal free distribution; Optimal foraging theory; Population; Statistical physics; Mathematics; Econometrics; Artificial intelligence; Physics; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":["sts","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002810687,0.0002593742,0.0002475915,0.0001119106,0.001939929,0.0002972164,0.001972596,0.0001496015,0.001593041],"category_scores_gemma":[0.0003829463,0.0001915586,0.0001519191,0.0003907881,0.0003453839,0.0001156645,0.008376847,0.001382869,0.0002352783],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005446602,"about_ca_system_score_gemma":0.0002206103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005367976,"about_ca_topic_score_gemma":0.008705682,"domain_scores_codex":[0.9948504,0.0008009207,0.0003768358,0.001053953,0.00178397,0.001133949],"domain_scores_gemma":[0.997807,0.0002644101,0.0001416822,0.001242712,0.0001324654,0.0004116901],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001658877,0.00363335,0.3538945,0.0006904586,0.0006077305,0.0006230643,0.01335389,0.3338833,0.002720268,0.2549603,0.01598759,0.01798663],"study_design_scores_gemma":[0.001640417,0.006485176,0.332536,0.0002947566,0.00005665244,0.00003895325,0.02397097,0.2255972,0.0005127745,0.2058046,0.201296,0.001766466],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9698336,0.0001973929,0.003582853,0.00411289,0.001139426,0.006131442,0.001708842,0.0001016646,0.01319191],"genre_scores_gemma":[0.9957199,0.000306565,0.0002520396,0.00003814244,0.0001536832,0.002091658,0.0001934464,0.00003279637,0.001211735],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1853084,"threshold_uncertainty_score":0.9996432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02073350566689042,"score_gpt":0.3608096286703431,"score_spread":0.3400761230034526,"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."}}