Comparison of geochemical data derived from till and lake sediment samples, Labrador, Canada
Bibliographic record
Abstract
Techniques for combining geochemical data from a till survey (2438 samples) and a lake sediment survey (17 447 samples) are assessed to determine a rigorous method for comparing the two sampling media. This study is based on five elements, (Cu, Ni, Fe, Pb and Zn) from overlapping geochemical surveys in Labrador, Canada. Two methods for comparing the till and lake sediment geochemical data are: (1) gridding and (2) nearest neighbour. Pearson correlation coefficients between media are low (<0.2) for Cu, Fe and Zn and only Ni has a correlation significant at the 95% confidence level (r 2 = 0.45). Results from gridding show slightly higher correlations. Differences are most evident at the extremes, as anomalously high element concentrations in one medium typically do not correlate with high values in the other medium. Correlations between media increase as distance decreases for Ni, Pb and Zn; however, no such trend is evident for Cu or Fe. The nearest neighbour method has several advantages: this procedure retains the original data and it permits calculation of statistics such as distance and direction between the points. The differences between the geochemical results from the two media highlight the synergistic value of multi-media geochemical sampling.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".