Bibliographic record
Abstract
The goal of this project is to find strategies by which architects, landscape architects and environmental designers can respond to marine ecosystem collapse. It uses Pacific Herring as a model for how to achieve this. Herring are hugely valuable to the Pacific Northwest Coast. They are Ecological Keystones, Economic Keystones, Cultural Keystones and Political Keystones as well. Unfortunately, herring stocks across multiple sites in British Columbia are collapsing, indicating a general trend towards all out Herring stock collapse (Levin at el. 2016). If the herring spawn abates, so too does this coastal ecosystem. The sites for this project is the herring spawn and anywhere that that occurs, which means the site is massive; extending from California to Alaska. Instead of responding to the project in its entirety, this project responds through what the Pacific Herring Summit has defined as the three greatest risks to herring stocks: petrochemicals, pipelines and tanker routes, disease from Fish Farming and the loss of spawning habitat from shoreline development. These became the guiding organization strategy by which the project moved forward. The project investigates the scale of risks associated with each of these threats to understand the problem traversing from the micro to macro. It investigated recent events, legislation and the key players that have been involved in ach one of these projects. Lastly it mapped these players and risks on to a series of regional systemic maps. The information was culminated in a design intervention responding to each risk. The first is a redesign of an approach to Fish Farming in the Nootka Sound on the West Coast of Vancouver Island. The second is a strategy for growing kelp forests in the Burrard Inlet to alter the renegotiation process of the Trans Mountain Pipeline. The third is a field station and hatchery that are used in the landclaims process to protect Flora Banks and Lelu Island from losing its spawning habitat. The project seeks to engage in themes beyond just those measurable and tangible by scientific organizations, exploring into the realms of the traditional and ritual, citizen action and engagement, stewardship and care for our environment and knowledge sharing both scientific and oral. The project proposes that design can be used as a negotiation tool, by utilizing the designer's ability to document, depict and draw, engage multiple key players and understand site as more than just a place.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.066 | 0.013 |
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".