Lake sediment geochemical methods in the Canadian Shield, Cordillera and Appalachia
Bibliographic record
Abstract
Abstract Lake sediment geochemistry has been used in Canada since the 1970s for mineral exploration and resource evaluation in glaciated regions of Appalachia, the Canadian Shield and the western Cordillera which are of low to moderate relief. Geochemical signatures of bedrock and mineralization within a lake’s catchment basin are commonly reflected in the chemical constituents of the organic-rich Holocene sediment which has been transported from source by a combination of mechanical and hydromorphic processes. Lake sediment geochemical surveys have been used successfully to discover base metal, Au, Mo, W, Sb, Sn, U, and REE mineralization. The scale of such surveys determines the size and density of lake sediment sampling. In regional surveys, large lithological targets such as greenstone belts or chemically distinctive intrusives can be identified by low density ( c. 1 per 10–15 km 2 ) sampling of relatively larger lakes. Smaller targets such as mineralized areas require higher density ( c. 1 per 4–5 km 2 ) sampling using a greater number of smaller lakes. Regional surveys are typically helicopter-borne, and employ a tubular grab sampler which permits rapid reconnaissance-scale coverage of large areas. Centre-basin profundal lake sediment, or gyttja, is an ideal sample medium because of its homogeneity and abundance of fine-grained organic matter, which complexes with many trace elements. Multi-element analytical techniques in common use include inductively coupled plasma emission spectrometry (ICP-ES), ICP mass spectrometry (ICP-MS) and instrumental neutron activation analysis (INAA). Reliability of data is assessed by analysis of known standards, site duplicates and sample splits. PC statistical software packages facilitate the interpretation of geochemical data, and desktop GIS packages aid in further interpretation and generation of multi-layer maps.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 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".