Berth 5 : An Argument for Landscape Design in Active Industrial Landscapes
Bibliographic record
Abstract
Landscape architects have demonstrated their ability to design in polluted and extractive landscapes through post-industrial landscape design. However, heavy industries continue to exist, pollute ecologies, displace wildlife and vulnerable people across the globe, albeit now far from the public eye. Yet these landscapes ultimately provide the foundational resources for human needs, comfort and culture. While de-carbonization of these landscapes is essential (yet outside the scope of landscape architecture) in the context of climate change, it over-simplifies the industrial ecosystems in which they exist. A de-carbonized future still requires extraction, processing and management of material streams. This graduate project outlines why landscape architect’s need to shift their focus to working heavy industrial landscapes. It reviews ways that landscape architects have begun to address these types of spaces and ultimately argues that both industry and designers need to be involved to find truly holistic solutions for industrial systems. Ultimately it highlights that more collaboration and understanding is required between industrial stakeholders, designers, and the public. The design portion of this project uses the iconic sulphur stockpiles of Metro Vancouver’s North Shore to foster this collaboration. Sulphur’s significance in both natural and industrial systems suggest human distinctions between these landscape require reevaluation. The design proposal displays ecological and industrial processes in on cohesive landscape, and invites the public to satisfy their existing curiosity of the sulphur piles, stand in awe at the scale of site features, and ultimately come away with a greater understanding of how integrated industrial landscapes are in our lives. Ultimately this project aims to inspire further collaboration between designers and industrial stakeholders. By implementing a project in a highly visible crucial node in the industrial fabric, subsequent system wide collaboration may become possible.
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 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".