An environmental comparison of foam-core and hollow wood surfboards : carbon emissions and other toxic chemicals
Bibliographic record
Abstract
Surfers in general are viewed as environmentally conscious individuals; however the boards that almost all of them ride are not considered green. In the past few years there has been a movement in the industry to find alternatives to the foam/fibreglass construction of surfboards. This movement was sparked by the closing of Clark Foam in 2005, the largest producer and supplier in the U.S. of polyurethane foam surfboard blanks. The plant was forced to shut down because of increasing environmental regulations. In 2008 a life-cycle analysis of the most common types of surfboards was performed to find out how this product was effecting the environment. There has been extensive research into new foam technology for boards since 2005, however, I believe that wood is a good alternative for surfboard construction. This paper includes a life-cycle assessment (LCA) to determine the emissions from wood board production and compares them to that of classic foam boards. The results show that wood surfboard production produces far less emissions of CO2, CO, SO2, NOx, VOC, and PM10 than foam surfboard production does. The LCA of wood boards included raw material production as well as production and assembly of the board itself. It can be concluded that from an environmental standpoint wood surfboards are a much better choice than the foam boards in use now.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".