{"id":"W4394021978","doi":"10.5281/zenodo.10257696","title":"Telemetry data from: Realized thermal niche approach eliminates temperature bias in 3 bioenergetic model estimates","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"thermodynamics and calorimetric analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Telemetry; Niche; Bioenergetics; Environmental science; Computer science; Ecology; Biology; Telecommunications","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007747064,0.0004584698,0.0005417878,0.0006021773,0.0007947615,0.0008601069,0.004489303,0.000484542,0.006656348],"category_scores_gemma":[0.001962764,0.0004376401,0.0001040016,0.001382922,0.0001834928,0.0001884879,0.004549932,0.001121514,0.004581483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002020875,"about_ca_system_score_gemma":0.00001942083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009774062,"about_ca_topic_score_gemma":0.000006935989,"domain_scores_codex":[0.9967667,0.0001640078,0.0005726931,0.00122087,0.0006845631,0.0005910958],"domain_scores_gemma":[0.9967989,0.000138951,0.0002871398,0.002303431,0.0002773522,0.0001942426],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008105504,0.0002569671,0.000001397486,0.000279278,0.0001972894,0.0000428273,0.00005470652,0.001379391,0.004526056,0.00003764172,0.9907307,0.002412698],"study_design_scores_gemma":[0.0008045887,0.00004011794,0.00002875396,0.0001566768,0.0002178278,0.00001823241,0.0002776901,0.1255191,0.0002892073,0.0001792306,0.8717871,0.0006814267],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.003441181,0.0005183151,0.000126638,0.00007176542,0.00006640486,0.0001831301,0.990043,0.0005895049,0.004960028],"genre_scores_gemma":[0.003328541,0.001158195,0.0003416171,0.0000429247,0.0002186649,1.747183e-7,0.9923527,0.001734476,0.0008227305],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1241397,"threshold_uncertainty_score":0.9998075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1101028888552907,"score_gpt":0.2990149363879275,"score_spread":0.1889120475326368,"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."}}