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Record W1838052677 · doi:10.1016/s0967-0653(97)81423-8

10.1016/s0967-0653(97)81423-8

2000· article· en· W1838052677 on OpenAlexvenueno aff
Theodore M. Hillyer, Eugene Z. Stakhiv, R. A. Sudar

Bibliographic record

VenueTime to knit · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsnot available
Fundersnot available
KeywordsShoreBreakwaterBeach nourishmentRecreationEnvironmental scienceEnvironmental resource managementStormEnvironmental planningBusinessEnvironmental protectionEngineeringGeographyOceanographyGeologyPolitical scienceMeteorologyGeotechnical engineering

Abstract

fetched live from OpenAlex

This study of the U.S. Army Corps of Engineers' (USACE) shore protection program was undertaken as a result of a request from the Office of Management and Budget (OMB). The investigation disclosed that the USACE shoreline protection program covers 8 percent of the nation's 2,700 miles of critically eroding shoreline and consists of 82 specifically authorized projects. Total expenditures, including periodic nourishment, for these 82 projects from 1950 to 1995 have been $731 million. When updated to 1995 dollars this expenditure becomes $1,662 million. Over the period of time covered by this study (l950-current) the program has shifted from primarily hard structures (groins, seawalls, breakwaters, etc.) to primarily soft beach restoration and nourishment through placement of sand. Beach restoration and nourishment is also the most environmentally compatible shore protection measure. The projects receive intense preconstruction coordination with environmental agencies to assure no long term adverse environmental impacts result from the projects. Over this same time period, as a result of Administration policy and law, the program has shifted from primarily recreation oriented to one of protection for storm damage reduction. From the standpoint of program cost and volumes of sand emplacements the evaluation of the long-term performance of the program shows that it is a well-managed and cost-effective program. Overall, costs were slightly less than estimated, and overall quantities of sand were slightly higher than estimated. Whether or not the projects are performing as expected from a benefit standpoint, however, is very difficult to determine. Because of the high variable and largely unpredictable nature of coastal storms, the actual storm damage reduction benefits of shore protection projects can differ greatly from those forecasted during planning and design. The key to the benefit-cost analysis is that the benefits are based on a probabalistic assumption that, over the period of analysis (generally 50 years), a comparable sequence of events will occur as in the past, causing a comparable level of property damages. Hence, the longer the period of record, the more likely that the estimated benefits will converge on the actual or measured benefits (and costs). One item of specific concern to OMB was that of induced development, i.e., do shore protection projects' lead to more growth and development in protected areas, and hence, ultimately to increases in storm damages rather than a reduction in damages. Three specific economic analyses were applied during the course of the study to determine whether USACE shore protection projects induce development in the areas they protect. These three complementary studies were: (1) a survey of beachfront community residents; (2) an econometric model of beachfront development;and (3) an econometric analysis of beachfront housing prices. None of these approaches could verify that there is a measurable induced development link. The analyses demonstrated that the primary determinant of development of beachfront communities is growth in beachfront demand based on rising income and employment in noncoastal areas, rather than the presence or absence of a shore protection project. In fact, there is limited public awareness of the Federal shore protection program, where Federal projects currently exist, and of the involvement of the USACE in reducing risks through project construction.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.008
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0050.006
Open science0.0040.004
Research integrity0.0070.003
Insufficient payload (model declined to judge)0.9920.993

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.

Opus teacher head0.004
GPT teacher head0.151
Teacher spread0.146 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations10
Published2000
Admission routes1
Has abstractyes

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