Gravity data acquisition and potential-field data modelling along Metal Earth's Chibougamau transect using geophysical and geological constraints
Bibliographic record
Abstract
The Metal Earth (ME) project aims to understand the underlying geological mechanisms that \ndifferentiate mineral endowments in Precambrian greenstone belts of the Canadian Shield. The \nME project acquires and collates various geological and geophysical data along 13 transects to \ncreate valid models of subsurface features in order to identify components that contribute to the \nmineralization processes that result in mineral endowment. \nIn this thesis, gravity observation along ~128 line kilometers in the Chibougamau transect is \nconsidered. The acquired data were checked for quality, processed to calculate the complete \nBouguer anomaly and combined with existing gravity data provided by the Geological Survey of \nCanada. \nGravity and compiled magnetic data were forward modelled along four sections and constrained \nby surficial geological observations, seismic sections, and petrophysical properties to estimate \nand improve the geometry and depth of plutonic bodies, and identifying the subsurface features \nsuch as dykes and faults. These improvements will help others to identify components that \ncontribute to mineralising processes.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".