Branchlines. Volume 23, number 2
Bibliographic record
Abstract
Dean's message (John L. Innes). MegaFlorestais 2012. Maja Krzic receives Soil Science for Society Award. Undergraduate enrolment and graduation at an all-time high. Forests and floods: Decades of scientific investigation gone awry. Legality requirements and the Chinese wood products industry. Measuring a tree’s carbon footprint. Aboriginal forestry: Visioning and payment for ecosystem services in Canada. Navigating a path through climate change. A day in the life of a field biologist (by Martha Essak). Estimating moose habitat suitability from satellite-derived indicators. 2 degrees, 2 years, 2 countries and twice the experience. Development & alumninews: Start a Knowledge Evolution with Research. John Richardson and Forest Management. Nicholas Coops and Remote Sensing. Jack Saddler and Biofuels. Private philanthropy can help. Reunions. Events. Class of 1961 creates a legacy program student award. Making a difference. A tribute to Irving (Ike) K Barber 1923 – 2012. (by Bill Bourgeois).
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.852 | 0.808 |
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".