Bibliographic record
Abstract
The Makah Tribe has fished in and around Neah Bay, Washington, for over 4,000 years. In early 2013, the Tribe recognized that deterioration of their aging timber fishing dock posed a large threat to the economy of this remote community at the northwestern-most point of the state and continental United States and selected a design team to replace the facility using the traditional design/bid/build method of procurement. In August 2013 during conceptual design, a portion of the decking collapsed, and the dock was closed because of the resulting unsafe conditions. As a result of a concentrated team effort by the Makah Tribe and their consultant team, all federal, state, and local permits for the work were obtained within 88 days of application submittal, and in-water construction began in late December 2013, four months after the collapse. By pre-ordering the piles and using a precast concrete deck system to minimize cast-in-place concrete, the dock structure was completed in three months and the facility, consisting of an access trestle, dock, fish-buying station, storage warehouse, and ice production plant, was operational by October 2014. This paper will provide an overview of the project and describe the challenges and solutions developed by the team that allowed the project to be completed only 10 months after the collapse.
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 | 0.001 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.059 | 0.009 |
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".