Asset Management Program Development and Implementation for the Port of Tacoma - Port of Tacoma, Tacoma, WA
Bibliographic record
Abstract
Asset management is an emerging management approach that has been embraced by both public and private sector infrastructure owners in the United States, Canada, and around the world. In the face of aging infrastructure and scarce capital, public infrastructure owners have been challenged to meet the growing demands and rising expectations of their customers and public stakeholders. The Port of Tacoma is implementing an Asset Management Program (AMP) to align asset and infrastructure decision-making with the Port's strategic plan. The asset management program coordinates the organization's assets to its business process and supports risk-based capital planning. The AMP integrates the full range of Port activities as they relate to the built and natural infrastructure of the Port, including maintenance management for the marine facilities and buildings, life cycle analysis, prioritized condition assessments, geographic information system, facility information database, and port business information and decision-making. These activities are supported by an integrated information system to provide useful information to various users and to facilitate data analysis. This presentation summarizes the deployment of a Pilot Asset Management Program at the Port. This initial phase looked at a limited subset of Port assets specifically focusing on three basic activities: ≤ Development of a Standardized Facility Assessment Methodology. Development of an approach to standardize facility condition and functionality assessments, including prioritization of assessments, definition and standardization of classification and data collection/delivery specifications (including GIS), and the execution of facility condition assessments at all major piers and wharves at the Port and a subset of upland facilities/buildings. ≤ Analysis of the Ports Information Systems and Data Requirements. An analysis was conducted of the Port's major information management systems (commercial software) and, independently, all internal stakeholders at the Port were consulted to establish a baseline of required data necessary for the management of physical assets. Included was an evaluation of integration with GIS. . As an interim step, a data repository (later dubbed an "Escrow Database") was created to capture much of the information collected during the Pilot Program and to enable testing of 'to-be' processes without encumbering existing systems. ≤ Business Process Integration and Development of a Risk-based Prioritization Decision Support Approach. Define and develop key performance indicators in alignment with Port business goals and objectives to directly support initiatives and long-range planning and provide risk-based input into capital planning and investment programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".