{"id":"W2122775856","doi":"10.4141/cjss08032","title":"Theta probe calibration for forest floor duff","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Soil Science","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Directorate for Biological Sciences","keywords":"Taiga; Forest floor; Boreal; Water content; Calibration; Environmental science; Hydrology (agriculture); Forestry; Soil science; Ecology; Geology; Geography; Mathematics; Soil water; Biology; Geotechnical engineering; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0007757106,0.0005718605,0.0003469993,0.0009012556,0.0003634435,0.0004340165,0.0008892869,0.000482266,0.005158605],"category_scores_gemma":[0.002957124,0.000274178,0.0002938843,0.00105094,0.0002232311,0.0006321772,0.0006655123,0.0004607557,0.001393328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004042934,"about_ca_system_score_gemma":0.0003662755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002401062,"about_ca_topic_score_gemma":0.003759817,"domain_scores_codex":[0.9992888,0.0001179303,0.00002370637,0.0001985106,0.0003187821,0.00005220827],"domain_scores_gemma":[0.9991223,0.0002403072,0.00008362671,0.0001628137,0.0003657252,0.00002515737],"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":[0.0005754635,0.0001020769,0.072119,0.0003200964,0.00008072545,0.0002066238,0.0005757816,0.004946696,0.7274212,0.001855517,0.00302702,0.1887698],"study_design_scores_gemma":[0.0001059955,0.0006482752,0.1793434,0.000116766,0.0001682236,0.001998116,0.0005646998,0.0650761,0.7092417,0.002060832,0.04054756,0.0001282781],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4557097,0.0007295248,0.52201,0.0001625989,0.0002263398,0.0003152802,0.002498773,0.00356988,0.01477793],"genre_scores_gemma":[0.8565351,0.0004674433,0.1362703,0.0001965104,0.00003011367,0.0003073598,0.001191439,0.0003837828,0.004617975],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005158605,"threshold_uncertainty_score":0.01725727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009774793085980235,"score_gpt":0.2114329771652522,"score_spread":0.201658184079272,"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."}}