Till geochemistry and sampling techniques in glaciated shield terrain: a review
Bibliographic record
Abstract
Abstract Till is a favoured sample medium for locating mineral deposits in glaciated shield terrains of Canada and Fennoscandia because it best reflects the primary composition of the bedrock source area. In the sampling phase, an important and costly component of till surveys, sample density, sample depth and sample method must be chosen according to the needs of the exploration program. Surface till sampling methods in forested areas differ from those used in permafrost terrain. However, in both areas, concentrations of labile ore minerals and their products of decomposition can be detected in the fine fraction (< 2 mm) of weakly oxidized till. In thin drift-covered areas, till samples are collected by hand excavation or trenching at < 5 m depth. In areas of thicker drift, more expensive methods such as reverse circulation rotary drills, rotasonic drills and portable drills are used to collect till samples at depth and to determine lateral and vertical variations in till geochemistry. Laboratory methods are an essential part of till geochemical surveys. The choices of the size fraction and analytical methods are determined by the nature and composition of the expected bedrock target, and by costs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".