Bibliographic record
Abstract
INTRODUCTION Lake Trout, the Boreal Shield and the Factors that Shape Lake Trout Ecosystems History and Evolution of Lake Trout in Shield Lakes: Past and Future Challenges Lake Trout in the Upper Great Lakes: Unique Management Challenges ENVIRONMENTAL FACTORS THAT AFFECT BOREAL WATERSHED ECOSYSTEMS Land, Water and Human Activity on Boreal Watersheds Impact of New Reservoirs Lake Trout (Salvelinus namaycush) Habitat Volumes and Boundaries in Canadian Shield Lakes Effects of Phosphorus and Nitrogen on Lake Trout (Salvelinus namaycush) Production and Habitat Dissolved Organic Carbon as a Controlling Variable in Boreal Shield Lakes Mercury Contamination Acidic Deposition in the Northeastern US: Sources and Inputs, Ecosystem Effects, and Management Strategies BIOLOGICAL EFFECTS AND MANAGEMENT REACTIONS Control of Harvest on Precambrian Shield Lakes Hatchery Stocking in Small Lakes: Factors Affecting Success Impacts of Invasive Species on Food Web Dynamics Effects of Forestry Roads on Reproductive Habitat and Exploitation of Lake Trout MODELS AND ISSUES ASSOCIATED WITH ECOSYSTEM MANAGEMENT Climate Change and Sustainable Lake Trout Exploitation: Predictions From a Regional Life History Model Monitoring the Health of Lake Trout Resource Synthesis Boreal Waters as Integrative Ecosystem Indicators Appendix: 1) Ecological Monitoring Sites for Boreal Shield Waters 2) Lake Trout Lakes of The Boreal Shield Ecozone of North America
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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.043 | 0.006 |
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".