Bibliographic record
Abstract
Ports authorities and mining companies work closely together to improve logistics infrastructure. For example, the Port of Saguenay on the St Lawrence Seaway, northeast of Quebec, is a major transit import port for fluorspar used in the nearby Rio Tinto Alcan aluminium plant. The port is currently investing in a major development, boosted by an injection of C$15m of federal government money. Regional mining company Arianne Resources (to be renamed Arianne Phosphate from May 2013), will focus on its phosphate mining interests and divest its holding in gold and base metals, vanadium, is set to be a major customer of the port (see pp.21-24). The company is focused on developing its Lac ^ Paul phosphorus-titanium project in Quebec and has identified Saguenay port and its C$34m, three-phase project to develop an intermodal industrial park at Grande Anse Marine Terminal, as its export hub. East Coast Canada, popularly known as Atlantic Canada, is the setting for a booming offshore oil and gas sector. With a growing number of world-class projects on stream or coming on stream, ports sited on the Newfoundland and Labrador coast are being upgraded to meet predicted increased demand for supporting the rigs with raw materials including industrial mineral-packed drilling muds. While Canada's export of coal, iron and grain shipments are vastly larger than its exports of industrial minerals (see p.45), the products are often exported from the same ports. Unless you're growing your infrastructure, you're not in the game. Simple as that, Geoff Machum, chair of the Halifax Port Authority, said. He believes the sector should to look at the longer term, and not be distracted volatile end-markets driven booming shale gas market in the US and economic problems across the EU.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.132 | 0.089 |
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".