Reassessment of elemental concentration data of sediments from the western delta of the Nile River
Bibliographic record
Abstract
The present study re-examines geochemical data produced by instrumental neutron activa- tion analysis (INAA) of sixty-two fired clay sed- iment samples from the western Nile delta in Egypt. The goal is to assess the strengths and weaknesses of principal component analysis (PCA) and bivariate data splitting (BDS), two widely used data analysis methods, in success- fully sorting differing sediment chemistries. Both PCA and BDS are performed using vari- ous data formats [i.e. original, calcium (Ca)- corrected, scandium (Sc)-normalized, or loga- rithmically (log10) transformed]. Both PCA and BDS are shown to sort differing chemistries well. While PCA has the advantage of speed, BDS has the advantage of providing specific chemical clarity and the opportunity to assess the degree of sand dilution more precisely. In PCA, the data format is semi-immaterial, while in BDS, different formats of the data may hin- der, rather than enhance, data interpretation, depending on the questions being asked.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".