Bibliographic record
Abstract
In the past few decades, numerous calamities have hit the coast of Japan and devastated cities around the nation. As communities rebuild their foundations, infrastructure needs to be reestablished. Houses and schools need to be rebuilt. This creates an opportunity for housing starts and other development, and subsequently, a demand for construction-ready materials. This demand would open the door to an increase in timber and lumber imports from foreign markets, such as Canada, the United States and Russia. In this essay, I focused on the years preceding and following the 1995 Kobe Earthquake and the 2011 Tohoku Earthquake, and looked for any possible trends in the timber and lumber markets within that timeframe. By comparing my findings for those years, and cross-referencing them with trends in non-event years, I did find a correlation between spikes in demand, import levels and disaster events. I believe that by studying past time periods, and the market flows and disaster events during those years, we are able to make educated predictions and better respond to shifts in demand. Through the writing of this paper, I have achieved a deeper appreciation of how global timber and lumber markets function, and how they are affected by events happening at the global level. Furthermore, I now understand how much housing starts influence the timber and lumber market, and the fluctuations that occur from year to year.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".