Copper-rich “Halo” off Lake Superior’s Keweenaw Peninsula and how Mass Mill tailings dispersed onto tribal lands
Bibliographic record
Abstract
Over a century ago, shoreline copper mills sluiced more than 64 million metric tonnes of tailings into Lake Superior , creating a “halo” around the Keweenaw Peninsula with a buried copper peak. Here we examine how tailings from one of the smaller mills (Mass Mill, 1902–1919) spread as a dual pulse across southern Keweenaw Bay and onto tribal L’Anse Indian Reservation lands. The fine (“slime clay”) fraction dispersed early and widely, whereas the coarse fraction (stamp sands) moved more slowly southward as a black sand beach deposit, leaving scattered residual patches. Beach stamp sands followed the path of sand eroding from Jacobsville Sandstone bluffs, mixing with natural sands and eventually adding onto Sand Point, at the mouth of L’Anse Bay. Dated sediment cores and a multi-elemental analysis of the buried Cu-rich peak in L’Anse Bay confirm a tailings origin. Copper concentrations are declining in the bay, yet copper fluxes remain elevated. The spatial and temporal studies underscore that enhanced sediment and copper fluxes around the Keweenaw Peninsula largely reflect historic mining releases. Mercury is correlated with copper, yet mercury concentrations and fluxes remain relatively low in Keweenaw Bay compared to nearby Superfund sites (Torch and Portage Lakes), perhaps reflecting the absence of smelters on Keweenaw Bay. Tribal efforts to remediate contamination are progressing, but are hindered by recent high water levels plus severe storms. The long-term repercussions of Mass Mill discharges caution against mine companies discharging even small amounts of tailings into coastal environments.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".