Bibliographic record
Abstract
Wood and timber were essential materials in maintaining the British war effort. Trench and tunnel revetments held up the western front, pit-props meant the mining industries could continue to supply munitions factories and railways, and the construction of camps housing new armies or migrant workers required massive amounts of timber. Although the First World War is seen as a pivotal event in the road to modernity, the list of vital uses for this ancient material is long. The woodlands of pre-war Britain were poorly managed for timber production, the nation relying very heavily on bulky imports. Improved self-sufficiency was therefore especially important as available shipping space declined as the war progressed. Although from mid-1916 a great deal of timber utilised on the western front was obtained from French forests by Canadian lumbermen, the woodlands, forestry profession and branches in the timber trade of the United Kingdom had to be mobilised and controlled to meet demand. The fact that stocks were eventually established, both in Britain and on the continent, and that shortages were never crippling to the war effort, illustrate how well these measures worked. 1
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.298 | 0.161 |
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".