Aquatic carbon flux data package for Oliver et al. 2017
Bibliographic record
Abstract
Overview This chemistry dataset was used to assess patterns in watershed export of dissolved organic carbon (DOC) concentrations, DOC flux, and characterization of dissolved organic matter (DOM) for preparation and publication of the manuscript: Oliver et al. 2017, Globally-significant yields of dissolved organic carbon from small watersheds of the Pacific coastal temperate rainforest, doi: 10.5194/bg-2017-5, Biogeosciences. Chemistry and discharge data were collected from the stream outlet stations of the seven focal watersheds at the Hakai watersheds observatory on Calvert and Hecate Islands, British Columbia Central Coast. Brief summary of methods For detailed information on sample collection and methods of analysis please refer to Oliver et al. 2017. Stream water grab samples were collected every 2-3 weeks (or ~monthly in winter) from May 2013 to July 2016 (n= 402) from stream outlets of the seven focal watersheds monitored within the Kwakshua Channel watersheds. Samples were run for analysis of dissolved organic carbon (DOC), iron (Fe), and optical characterization of dissolved organic matter (DOM) using spectrofluorometry. Excitation emission matrices (EEMs) were used in parallel factor modeling (PARAFAC) to deconstruct and identify individual fluorophore components of DOM. Model output from this PARAFAC analysis can be found online at www.openfluor.org. Stream discharge was measured by developing stage discharge rating curves at a fixed hydrometric station in proximity to the outlet. DOC flux was estimated using multiple regression methods in the R package rloadest by combining DOC concentrations with 15-minute continuous stream discharge from each watershed. Loads for October 1, 2015 through April 30, 2016 are presented for each watershed in average kilograms per day. Further details on the development and analysis of stage discharge rating curves and multiple regression models used for estimating DOC flux can be found in Oliver et al. 2017 Oliver, A.A., S.E. Tank, I. Giesbrecht, M.C. Korver, W.C. Floyd, P. Sanborn, C. Bulmer, and K.P. Lertzman. 2017. Globally-significant yields of dissolved organic carbon from small watersheds of the Pacific coastal temperate rainforest. Biogeosciences Discussion paper.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.086 | 0.061 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".