Bibliographic record
Abstract
Consumers' decisions on plumbing material selection are dictated by various factors, including state and federal regulations, service providers, and individual household preferences.The regulations and standards of the federal, state, and local governments have major impacts on the plumbing material chosen for installation in a private house.For example, the use of plastic plumbing material, such as PEX, has been approved in all U.S. states except for California and Massachusetts, where the material installation requires local jurisdiction acceptance.Similarly, in some parts of Florida, PEX is preferred due to the seriousness of pinhole leak 1 problems (NSF, 2008).These regulations influence services provided by plumbers, material producers (e.g.pipe manufacturers, interior coating providers), and water utility companies.For example, general contractors are the primary decision-makers of plumbing material installation in new houses, while utility companies respond to corrosion threats by adding corrosion inhibitors to drinking water treatment.Consequently, all service providers influence consumer decisions, regarding the best plumbing material for private properties.Homeowners have an important stake in finding plumbing system appropriate for their households, and they should rely not only on expert advice, but also acquire information on plumbing material attributes such as price, health impact, longevity, and corrosion resistance, in order to make informed investment decisions about plumbing systems for their homes.For example, health effects, water taste and odor have been found to be the most important factors in consumers' evaluations of plumbing material for home use (Lee et al., 2009).Additionally, households are willing to pay up to $4,000 when guaranteed a leakfree plumbing system for 50 years (Kleczyk et al., 2006).Information on consumer preferences for drinking water plumbing attributes can be useful not only to individual households, but also to policymakers, program managers, water utilities, and firms with interests in drinking water infrastructure.1 Pinhole Leaks are a small holes that commonly are caused by pitting corrosion, a type of corrosion concentrated on a very small area of an inner pype.In most cases, pinhole leaks are hard to detect, if they are visible, they appear as green, wet area on pipe and porcelain fixtures (Kleczyk & Bosch, 2008).www.intechopen.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".