Use of georeferenced data to study clustering in the primary wood products industry of the US South
Bibliographic record
Abstract
Forest business clusters offer a unique opportunity to improve competitiveness of the forest products sector and stimulate economic development in rural areas. This study utilized georeferenced data on the location of primary wood products manufacturers in the US South to examine spatial clustering within this industry. Independent spatial analyses, F-hat and L-hat, and a χ 2 statistic provided evidence of regional clustering. A count data model was used to determine location preference of primary wood-using mills and identify factors promoting industry clustering. It was determined that access to a labor pool, low cost of primary inputs, presence of related industries, adequate transportation infrastructure, and low land values positively influenced clustering among primary wood-using mills. A marginal analysis indicated that counties with adequate transportation infrastructure and presence of related industries were most likely to attract new primary forest products manufacturers. These two factors increased the predicted number of sawmills by 26.83% and 22.65%, respectively. Increases in prices of logs and energy can deter the spatial aggregation of wood-using mills. Results provide evidence that public investments in infrastructure can have an important role in attracting wood products industry firms.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".