Modernization, meet paradigm shift. Paradigm shift, meet chaos*
Bibliographic record
Abstract
The adoption of genomic technology and the use of improved seeds are expected to improve timber productivity in Alberta. However, this improvement will need to take place within the confines of the public-private nature of the sector where 93% of the total forest area is publicly owned. The purpose of this study is to explore the extent to which a timber harvest policy known as the allowable cut effect can affect the welfare outcomes of adopting genomics-assisted tree breeding. Using the forest industry of Alberta as the empirical setting, the economic returns to the adoption of this new breeding technology in lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia Engelm.) and white spruce (Picea glauca (Moench) Voss) are calculated by estimating a timber supply model and a spatial equilibrium model. Under certain policy and technology improvement scenarios, the economic returns are negative, which would result in non-adoption of the technology. However, under other feasible conditions, the payoffs of genomics-assisted tree breeding research are large and positive. These results illustrate the important role that government policies can have on the returns to adopting new technologies.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".