7-component PARAFAC model, dataset, and matlab code of aquatic dissolved organic matter composition in North American Great Lakes Region
Notice bibliographique
Résumé
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 & 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 & 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) &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tutorial of Stedmon and Bro (2008).Note: DOMFluor is no longer compatible with current versions of Matlab and has been replaced by the drEEM toolboxDOM samples.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>
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,035 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».