Evolution Mechanism of Off-site Construction Ecosystem Based on the LotkaäóñVolterra Model: A Case Study
Bibliographic record
Abstract
Off-site construction (OSC) is an alternative method to conventional construction for solving problems of high energy consumption, high pollution, and poor efficiency. OSC is a reform trend for the global construction industry. The emergence of OSC can influence the production relations in traditional construction industry chain, thereby changing the roles of stakeholders in such new construction sector. Given that most developing countries at present are still in the initial stage of adopting OSC, their construction industry is far from forming healthy and symbiotic ecosystem and highly efficient industry chain. Therefore, the scientific and rapid development of OSC becomes restricted. This study analyzes the mechanism of OSC ecosystem, reduces the vague understanding of OSC by stakeholders, and provides a reference for the strategy planning of stakeholders. From the perspective of bionics, this study aims to (1) establish an OSC ecosystem based on the theory of ecology and delimit the role of stakeholders in the OSC ecosystem, and (2) establish a LotkaäóñVolterra model for the OSC evolution. The OSC development in Beijing is used as an example. Data are collected and models are verified to discuss the state, trend, and turning point of the OSC evolution. The findings of this study can help stakeholders in comprehensively understanding the inherent historical development of OSC and provide a reference for the government decision making.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".