Introduction to the GSC MITE Point Sources project
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Point Sources project of the Geological Survey of Canada's Metals in the Environment Initiative (1997 - 2002) examined the distribution of metals in various environmental sampling media around three Canadian metal smelters at Rouyn-Noranda (Quebec), Belledune (New Brunswick), and Trail (British Columbia). This bulletin contains eleven papers on the studies made at Rouyn-Noranda and one paper on work at the Belledune smelter. Regional surveys of snow, soil, lake sediment, peat, and vegetation were used to characterize and understand metal dispersal around the Horne smelter at Rouyn-Noranda. Metals emitted by the Horne smelter are transported by the atmosphere and radially dispersed around this point source. The resulting smelter footprint, i.e. where metal concentrations are significantly higher than ambient background levels, shows exponentially decreasing values with increasing distance from the source in various environmental media (snow, soil, peat, lake sediment, trees). Snow data provide information about metal loading (deposition rates) in winter conditions. The most recent growth of peat hummocks provides independent estimates of metal loading over one year. Loading data (modelled as a function of distance from the smelter) provide estimates of the total tonnage of metal deposited within (and close to) the footprint for comparison with emission data. The distribution of metals in marine sediments near the Brunswick lead smelter is controlled by both atmospheric and oceanographic processes. The influence of the emissions on metal levels in coastal sediments near Belledune extends at least 20 km from the smelter.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it