Distribution of saturated hydrocarbons in unweathered and erosional landforms
Bibliographic record
Abstract
Badlands are worldwide erosional landforms. The \nformation of different badland morphologies is due to \nthe type of sedimentary bedrock, especially texture \nand cementation degree, as well as climate. \nIn this study badlandes mudstones which have high \nsilt and clay contents from different locations in Italy, \nSpain and Canada (Figure 1) were investigated from \norganic-geochemical point of view. It is known that \nvegetation is commonly identified as a significant \ncontrolling mechanism of land degradation in \nsensitive, semi-arid environments [1]. \nTotal of 18 samples were analysed from 9 different \nlocations. From each location unweathered mudstone \nand crust were taken. Qualitative and semiquantitative composition of the mineral part of \nsamples was determined using X-ray diffractometer. \nAdditionally, chemical properties such as pH, EC, Eh, \nSAR are determined. The content of organic carbon \n(Corg), was determined by elemental analysis after \nremoval of carbonates with diluted hydrochloric acid \n(1:3, v/v). Soluble organic matter, bitumen, was \nextracted from sediments using the Soxhlet extraction \nwith an azeotrope mixture of dichloromethan and \nmethanol (88:12, volume %). Isolation of the \nsaturated and aromatic fraction was done using \ncolumn chromatography. Organic compound were \nanalyzed by gas chromatography-mass spectrometry \n(GC-MS) technique in the fractions of saturated \nhydrocarbons. \nMany study has shown that clay mineralogy is \nextremely important for the behaviour of different \nmaterials undergone weathering/erosional processes as well as smectite-containing sediments have been \nshown to be more erodable [2]. Additonally, the \npresence of enough amount of organic matter, iron \nand aluminum oxides causes to make marls durable \nwhile, sodium ions cause more erosion associated \nwith dispersed clay particles [3]. \nIn this study, it was observed that major changes in \nthe distribution of saturated hydrocarbons occurred in \nsamples containing smectite compared to those \nsamples where mentioned mineral was not identified. \nThis confirms that the presence of smectite is crucial \nfactor for changes inorganic and organic matter \nduring erosional processes. \nMentioned changes in distribution of saturated \nhydrocarbons are most pronounced for n-alkanes, \nwhereby higher odd-numbered n-alkanes are most \nsensitive during erosive processes. For that reason \nparameters which reflect the ratio of higher and lower \nn-alkanes (for example, TAR, TAR/MAR, CPI) \ndecrease in eroded samples compared to unweathered \nmudstones. \nSignificant changes in distribution of polycyclic \nalkanes of sterane and terpane types were not \nobserved.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".