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7-component PARAFAC model, dataset, and matlab code of aquatic dissolved organic matter composition in North American Great Lakes Region

2023· dataset· en· W6921100103 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2023
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsMATLABExcitation wavelengthAbsorbanceAnalytical Chemistry (journal)WavelengthFluorescenceFluorescence spectrometryRaman spectroscopy

Abstract

fetched live from OpenAlex

DOM UV-visible absorbance and fluorescence characteristics were determined for 971 samples collected from a variety of surface water locations throughout the Great Lakes Region and Southern Ontario &amp; Quebec Regions of North America.<b>Included in this repository</b>The final saved Matlab data project for the model (11Jan2010Model7Ex250Em300AllData.mat)The original EEMs read into Matlab (OriginalData.mat &amp; matlab.mat)The EEMs after removal of specific wavelengths and samples (CutData.mat)The split-half validation of the full model (SplitHalfTest1.mat)The Output of the full model and split-half validation (Model7Ex250Em300AllData_ResultsOutput.xlsx<br>SpiltHalfValidation_FinalModel7.xls) &amp;Contour plot images of the full model<b>Data used to construct the original parallel factor analysis model (PARAFAC) were treated as follows:</b>Light absorbance was measured from 800 to 230 nm using a lambda 25 Perkin Elmer spectrophotometerFluorescence excitation emission matrix (EEM) scans were measured using a Varian Cary Eclipse fluorometerScan were made from 230 to 500 by 5 nm excitation and 270 to 600 by 2 nm emission with a bandwidth of 5 nm and at a scanning interval of 0.25 seconds.EEMs were corrected fully for inner filter effects, Milli-Q background, blank subtracted and instrument bias following the recommendations of (Cory et al. 2010; Murphy et al. 2010).Relative fluorescence units were converted to Raman units using the area under the Milli-Q scatter peak at 350 nm excitation.<b>For Parallel factor analysis (PARAFAC) modeling</b>The DOMFluorv1_7 toolbox in Matlab 2007b (Mathworks) was used for analysis following the PARAFAC tutorial of Stedmon and Bro (2008).Note: DOMFluor is no longer compatible with current versions of Matlab and has been replaced by the drEEM toolboxDOM samples. Prior to modeling, EEMs were trimmed to 250–500 excitation and 300–600 emission, first-order scatter was removed, and outlier EEMs were deleted.The PARAFAC model was validated using split-half analysis and Tucker congruence.The model was originally published in Williams, C.J., P.C. Frost and M.A. Xenopoulos. 2013. Beyond best management practices: Pelagic biogeochemical dynamics in urban stormwater ponds. Ecological Applications 23: 1384-1395. and is published on OpenFluor as GreatLakesRegion to be compared with other PARAFAC modelsThis model has been used in multiple manuscripts and used to fit over 2000 EEMs collected after the initial model generationKing, S.S.E., P.C. Frost, S.B. Watson, and M.A. Xenopoulos. Transitions in dissolved organic phosphorus and dissolved organic carbon across a river-lake transect.Begum, Most S., M. Kadjeski, C. Fasching and M.A. Xenopoulos. Temporal variability of dissolved organic matter composition export in streams.Klemet-N’Guessan, S., M. Taskovic, N.J.T. Pearce and M.A. Xenopoulos. Fine ecological scales highlight the nonlinear relationship of animal nutrient excretion with dissolved organic matter.Pearce, N.J.T., J.H. Larson, M.A. Evans, S.W. Bailey, P.C. Frost, W.F. James, and M.A. Xenopoulos. 2023. Dissolved organic matter transformations in a freshwater rivermouth. Biogeochemistry https://doi.org/10.1007/s10533-022-01000-zWilliams, C.J., P.C. Frost, B. Ginn, D. Lembcke, J. Marsalek, and M.A. Xenopoulos. 2023. Add a dash of salt? Effects of road de-icing salt (NaCl) on benthic respiration and nutrient fluxes in freshwater sediments. Limnetica. DOI: 10.23818/limn.42.17Pearce, N.J.T., Dyczko, J.M. and M.A. Xenopoulos. 2022. Carbon and nutrients regulate greenhouse gas fluxes from oxic stream sediments. Biogeochemistry 160: 275-287 https://doi.org/10.1007/s10533-022-00955-3Pearce, N.J.T., J.H. Larson, M.A. Evans, P.C. Frost, and M.A. Xenopoulos. 2021. Episodic nutrient addition affects water column nutrient processing rates in river-to-lake transitional zones Journal of Geophysical Research: Biogeosciences 126: e2021JG006374; DOI: 10.1029/2021JG006374Kadjeski, M., C. Fasching and M.A. Xenopoulos. 2020. Synchronous biodegradability and production of dissolved organic matter in two streams of varying land use. Frontiers in Microbiology 11: 568629 (doi: 10.3389/fmicb.2020.568629).Larson, J.H., W.F. James, F.A. Fitzpatrick, P.C. Frost, M.A. Evans, P.C. Reneau, and M.A. Xenopoulos. 2020. Phosphorus, nitrogen and dissolved organic carbon fluxes from sediments in freshwater rivermouths entering Green Bay (Lake Michigan; USA). Biogeochemistry 147: 179-197.Fasching, C., C. Akotoye, M. Bižić-Ionescu, J. Fonvielle, D. Ionescu, S. Mathavarajah, L. Zoccarato, D.A. Walsh, H.-P. Grossart and M.A. Xenopoulos. 2020. Linking stream microbial community functional genes to dissolved organic matter and inorganic nutrients. Limnology and Oceanography. 65: S71-S87.D’Amario, S.C., D.C. Rearick, C. Fasching, S. Kembel, E. Porter-Goff, D. Spooner, C.J. Williams, H.F. Wilson and M.A. Xenopoulos. 2019. The prevalence of non-linearity and detection of ecological thresholds across a land use gradient. Scientific Reports 9: 3878 (DOI: 10.1038/s41598-019-40349-4).Williams, C.J., P.C. Frost, A.M. Morales-Williams, J.H. Larson, W.B. Richardson, A.S. Chiandet and M.A. Xenopoulos. 2016. Human activities cause distinct dissolved organic matter composition across freshwater ecosystems. Global Change Biology 22: 613-626.Larson, J.H., P.C. Frost, M.A. Xenopoulos, C.J. Williams, A.M. Morales-Williams, J. Vallazza, J.C. Nelson and W.B. Richardson. 2014. Relationships between land cover and dissolved organic matter change along the river to lake transition. Ecosystems 17: 1413-1425Williams, C.J., J.E. Van Eindhoven, E. Theberge, and M.A. Xenopoulos. 2014. Effects of golf course facilities on stream function in anthropogenically impacted streams. Anthropocene 3: 51-60.<br>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.250
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.035

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.

Opus teacher head0.054
GPT teacher head0.287
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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".

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Citations4
Published2023
Admission routes1
Has abstractyes

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