Bibliographic record
Abstract
Twenty-five percent of the grain production in Canada is located closer to the Port of Churchill than any other port. Churchill provides unique opportunities for the export of manufactured, mining, agricultural and forest products, as well as the importation of minerals, steel, building materials, fertilizer, and petroleum products for distribution in Central and Western Canada. This research aims to help extend the port of Churchill’s shipping season by determining shipping routes using geographic information systems, remote sensing and long-range ice forecasting. The need to extend its shipping season is attributable to the harshness of the climate in the Hudson Bay area. Extensive ice coverage throughout the Hudson Bay diminishes the shipping season to approximately 4 months of the year (June 23rd to November 12th). This short shipping season calls for long-range ice forecasting for shippers and the port authority to plan the large. The Canadian Ice Service (CIS) currently provides these forecasts through analog methods. In order to improve such forecasting techniques, the CIS is embarking on creating statistical and spatial models by comparing historical sea-ice with global atmospheric and oceanographic patterns. These modeling efforts will provide a forecast for the entire Hudson’s Bay. This work will feature a suitability model with a spatial-temporal analysis that predicts the path through seasonal sea-ice. Once completed, a least cost path analysis shall be conducted using the suitability model to determine the best viable routes for ships to navigate to and from the port of Churchill. This paper will demonstrate a few concepts in sea-ice prediction with GIS and will introduce the fundamental components for a thorough analysis.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.642 | 0.583 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".