Estimating the Effects of Covid-19 and Softwood Lumber Prices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
During the Covid-19 pandemic, markets observed unprecedented changes in U.S. and Canadian softwood lumber prices and their volatility. In this paper, we employ an event-based model to estimate the impact of Covid-19 on the prices of softwood lumber, utilizing a Regression Discontinuity design model to investigate the potential causal effect of Covid-19 on softwood lumber prices. Our econometric analyses serve to provide evidence that softwood lumber price increases during the pandemic were not completely random but could instead be attributed in part to variations in recent global and regional events. Our research highlights the need for the adoption of robust and adaptable strategies and provision of information important for risk assessment and decision-making in industries that rely on softwood lumber inputs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it