Bibliographic record
Abstract
The Province of Nova Scotia is committed to protecting important elements of its natural environment and cultural heritage. As Minister of Natural Resources, I am responsible for ensuring that our provincial parks, and the heritage values they contain, are managed in perpetuity for the benefit of present and future generations. In this context, I am proud to present the McNabs and Lawlor Islands Provincial Park Management Plan. Moulded by glaciers, reshaped by unrelenting coastal processes, and with a human presence dating at least 1,500 years before present, McNabs and Lawlor Islands Provincial Park contains provincially and regionally significant natural and cultural heritage values and provides important recreational opportunities. Situated in the heart of Nova Scotia’s largest metropolitan area, these islands also provide important opportunities for outdoor education and have the potential to contribute significantly to the quality of life of residents within the Halifax-Dartmouth metropolitan area as well as other Nova Scotians and out-of-province visitors. The Province began to assemble the land base for McNabs and Lawlor Islands Provincial Park in the 1970s in response to residents ’ concerns about the need to protect these outstanding islands from inappropriate development and use. The Provincial Parks Act and this park management plan will
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.118 | 0.030 |
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".