Bibliographic record
Abstract
There are lots of floating houses in San Francisco, Seattle, Portland, and Vancouver area. Some floating houses have long history and others are fairly new. Residents in floating home community enjoy the peaceful and comfortable atmosphere within natural environment and good neighbors. Floating house can be defined as a building for living space that floats on water with floatation system, is moored in a permanent location, does not include a water craft designed or intended for navigation, and has a premises service system served through connection by permanent supply/return system or has self-supporting service facilities for itself. This paper aims to discuss the resilient features of floating houses through the samples and to suggest some reference ideas for new resilient living projects around the water space. Research method includes the site-visits of floating house and resilient living, the review of related literatures, and the navigation of some homepage. Various resilient features of floating house such as resilient against climate change, energy usage, difficulties of relocation, environment pollution, and social uneasiness are to be considered. Floating house on the water is endurable to a rise in water level due to climate change, is easy to employ various renewable energy system because of no obstacles in water space, is movable and relocated to different places in need and can be reused by different people for a long time, can discharge the minimum construction waste and noise because of factory manufacturing through prefabrication and modular system, and is socially secure against crime and psychologically comfort due to residents’ closeness, entrance control and natural environment. With the secure of safety against natural disaster and the evacuation from fire, floating house can be regarded as one of the most resilient living alternative, especially around the water region.
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 | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.006 |
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".