Bibliographic record
Abstract
Bringing woodlot owners together under one roof in Nova Scotia has always been a challenge.There are many organisations across the province with differing memberships, mandates, and objectives.Perhaps even more critical to the achievement of sustainable forest management on private woodlands is the fact that the majority of Nova Scotia's 33 000 landowners are not members of any organization; as a result, they have little access to information and expertise about private woodland management.On November 21, 2002, the Nova Forest Alliance (NFA) announced the Woodlot Info Shop (WISh).This new pilot project creates the opportunity for technical and operational experts to work with woodland owners to assemble upto-date information for landowners.The Woodlot Info Shop will bring current and evolving woodlot information to one place -a first stop for all woodlot information.It is the interface between Nova Scotia's vast population of woodlot owners and the many associations working to provide them with information and services.An extensive working group has been assembled to guide the project.The group is co-chaired by two past Woodlot Owners of the Year, Don Moore and Mary van den Heuvel.They are encour-'Well, you can't chuck wood, so why fret about it!"The Forestry Chronicle Downloaded from pubs.cif-ifc.orgby 184.174.3.125 on 08/16/23 For personal use only.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.414 | 0.129 |
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".