Density and magnetic susceptibility of major rock types within the Abitibi greenstone belt: a compilation with examples of its use in constraining inversion
Bibliographic record
Abstract
Geophysical inversions give non-uniqueness solutions and unless constrained by appropriate initial values and geological constraints can give unrealistic results. One of the critical constraints can be the physical property values of different lithologies. We have compiled a density and magnetic susceptibility database consisting of thousands of measurements collated from different organisations and/or projects across the Abitibi greenstone belt. Statistical tools (histograms, quantile-quantile probability plots and boxplots) are applied to characterise systematically major and minor lithologies. We observed that the magnetic susceptibility frequently has a bimodal distribution, while density is typically unimodal. Our results are summarized in a table that includes the representative mean (or median) and a range of acceptable values. These values can be used to better understand the regional geology, but in this paper, we used the tabulated properties in a geophysical/petrophysical inversion of gravity data from the Chicobi area in the Abitibi subprovince to show the level of improvements that the petrophysical constraints can add to an unconstrained model. When our density database is used to seed the initial guess in a gravity inversion, an anomalous zone becomes apparent that was less evident on an unconstrained inversion.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.009 |
| 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) | 0.003 | 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".