{"id":"W3134408901","doi":"10.20944/preprints202103.0223.v1","title":"Gas Exchange, Water Use Efficiency, and Biomass Partitioning Among Geographic Sources of <em>Acer saccharum</em> subsp. <em>saccharum</em> and subsp. <em>nigrum</em> seedlings in Response to Water Stress","year":2021,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Plant-Derived Bioactive Compounds","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sugar; Maple; Biomass partitioning; Saccharum; Water-use efficiency; Evapotranspiration; Saccharum officinarum; Transpiration; Seedling; Aceraceae; Horticulture; Environmental science; Agronomy; Botany; Stomatal conductance; Biology; Shoot; Photosynthesis; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.007142869,0.004816743,0.005254109,0.00384229,0.001527238,0.002443867,0.004441577,0.00397211,0.003514644],"category_scores_gemma":[0.001941637,0.004604195,0.001756793,0.002161679,0.00180647,0.002124556,0.01332958,0.004311034,0.0005805547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001396885,"about_ca_system_score_gemma":0.0007418318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007906965,"about_ca_topic_score_gemma":0.008936041,"domain_scores_codex":[0.9734861,0.002373683,0.005220586,0.00876933,0.004384642,0.005765663],"domain_scores_gemma":[0.9846744,0.001626404,0.002524799,0.006473471,0.002312005,0.002388956],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003420189,0.001324141,0.2658691,0.002448721,0.001400619,0.00104125,0.02275177,0.0007422314,0.7001529,0.00002103684,0.0002048601,0.0006232519],"study_design_scores_gemma":[0.004775655,0.0002476558,0.2940403,0.004163158,0.000951736,0.0004020255,0.003042676,0.001254441,0.6798266,0.0002978616,0.00593943,0.005058456],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9856591,0.004233406,0.0002154951,0.000780776,0.001514635,0.003269608,0.002424494,0.001173969,0.0007284967],"genre_scores_gemma":[0.9877741,0.002795458,0.0006128521,0.0003353418,0.0008487286,0.001482064,0.002290579,0.0008565543,0.003004322],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02817125,"threshold_uncertainty_score":0.9997727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03459333192672658,"score_gpt":0.2603187450733953,"score_spread":0.2257254131466687,"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."}}