{"id":"W4393817100","doi":"10.5281/zenodo.5758218","title":"Predicting methane emission in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks","year":2021,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Odor and Emission Control Technologies","field":"Chemical Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Guelph","funders":"","keywords":"Near infrared reflectance spectroscopy; Reflectivity; Methane; Dairy cattle; Artificial neural network; Methane emissions; Spectroscopy; Environmental science; Diffuse reflectance infrared fourier transform; Animal science; Materials science; Near-infrared spectroscopy; Chemistry; Biology; Optics; Artificial intelligence; Computer science; Physics; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.000529971,0.002928383,0.0011572,0.002771963,0.001478661,0.001114651,0.002519976,0.001413363,0.02400126],"category_scores_gemma":[0.001693108,0.0006393139,0.001770832,0.003672197,0.0005509069,0.0004347337,0.0007446177,0.001012683,0.01156582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005470862,"about_ca_system_score_gemma":0.006661506,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.822633,"about_ca_topic_score_gemma":0.9105867,"domain_scores_codex":[0.9995682,0.0000273238,0.00002315529,0.0001204064,0.000151187,0.0001097716],"domain_scores_gemma":[0.9991232,0.0001601552,0.00004095589,0.0001092314,0.0004765645,0.00008998819],"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":[0.0005936407,0.0003019491,0.01789051,0.00135798,0.000358856,0.0001775802,0.00005839517,0.01806506,0.001427139,0.0005498574,0.9326834,0.02653564],"study_design_scores_gemma":[0.001273828,0.0002487946,0.1835194,0.0008482576,0.0007448134,0.0003366476,0.000741546,0.04933188,0.006859968,0.002577901,0.7531847,0.0003322749],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.006984548,0.0002219618,0.0001817989,0.00005934029,0.00004211948,0.00002807684,0.9912595,0.0003616989,0.0008609816],"genre_scores_gemma":[0.005234732,0.0001141499,0.0005283368,0.00001992082,0.000006349129,0.00004757072,0.9927261,0.00002993203,0.001292924],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.177367,"threshold_uncertainty_score":0.356823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03474482137343374,"score_gpt":0.2612640039922797,"score_spread":0.2265191826188459,"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."}}