Northern lights : a history of Thunder Bay Hydro / by David Leo Black.
Bibliographic record
Abstract
Electricity has played an important role in the economic and social development \nof Ontario cities, and Port Arthur and Fort William located at the head of the Great \nLakes are no exception to this rule. Industrial development depended on an adequate \nelectric power supply, especially for the pulp and paper industry, the industrial mainstay \nat the Lakehead. Safety in the city streets and a high quality of life for the citizens were \nalso provided by electric lighting and numerous electric appliances. There are few \nhouseholds or businesses today that do not have access to electrical power; in this way, \nit has touched each of our lives. This paper examines the history of the prime provider \nof electric power at the Lakehead, Thunder Bay Hydro and its predecessors. For one \nhundred and ten years this area has been served by the hydro electric Commissions of the \ntwo cities. For the first four decades of their existence, roughly 1910 until the end of the \nGreat Depression, the Port Arthur Public Utilities Commission and the Fort William \nHydro-Electric Commission were influenced greatly by the inter-city rivalry. The two \ncites are geographically isolated from other cities but in close proximity to each other \nand this set the stage for their rivalry. They competed for such things as industries and \nelectric power. After the Great Depression began, however this rivalry subsided, where \nelectric power matters were concerned.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.018 |
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".