Geochemical and mineralogical composition of grab and core sediments from Inle Lake (Southern Shan State, Myanmar)
Bibliographic record
Abstract
Lake sediments were sampled in March 2014 in 16 locations selected so as to cover the whole lake area. At each lake site, grab sediment samples were collected with a stainless-steel, Ponar type sampler; in addition, at 5 stations, sediment cores were collected with a sampler prototype (handmade), which ensures careful recovery of the sediment-water interface. Cores ranged from 55 to 85 cm in length and were cut into 5 cm slices on the same day of collection.The mineralogical analyses were carried out by X-ray Powder Diffractometry (XRD) performed both on natural samples, and on treated samples to identify the clay minerals, using the standard procedure of ethylene-glycol saturation followed by heating at 550°C for the identification of the swelling clay minerals. Mineralogical results are expressed in %.The chemical composition (major and trace elements) was analysed by "Near Total" Digestion ICP/MS (Code UT-4M) at the Activation Laboratory, Canada. Analysed elements include Ca, Mg, Na, K, Al, Fe, P, S, Ti (in %), and Sr, Ba, Rb, Li, Rb, Cs, Mn, Cd, Co, Cr, Cu, Ni, Pb, Mo, V, Zn, W, Tl, Bi, Sn, As, Sb, Ag, Au, Sc, Ti, Y, Zr, Nb, La, Ce, Hf, Ta, U, Th (in mg/kg).The dataset includes 6 tables:Grab samples (16 locations, approx. depth 0-10 cm)Core 2 (UTM Long. 47Q 0283122 Lat. 2280515, depth 5-80 cm)Core 3-1 (UTM Long. 47Q 0282707 Lat. 2276910, depth 5-55 cm)Core 4-2 (UTM Long. 47Q 0281190 Lat. 2269152, depth 5-75 cm)Core 4-3 (UTM Long. 47Q 0282356 Lat. 2268878, depth 5-60 cm)Core 6 (UTM Long. 47Q 0282396 Lat. 2265350, depth 5-85 cm)
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 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".