Bibliographic record
Abstract
Lighthouses are the heart and soul of hundreds of communities across Atlantic Canada; they were integral to the survival and growth of their people while acting as a safety net around the coasts. These communities no longer require lighthouses to guide their fishing vessels and passing ships to safety. Now that their primary purpose is no longer required, many of these buildings are being lost. This is due to a lack of resources to keep them properly maintained, especially when faced with the increasing frequency of harsh weather conditions due to climate change. Their histories and experiences tied to them are usually kept isolated from each other, tucked away in an old photo album or journal drifting into obscurity. \nThis research aims to provide new ways to preserve the histories and stories associated with these buildings in a way these small communities can access and afford while allowing a broader range of people across the world a glimpse into this unique community. To achieve this, new emerging accessible technologies have been utilized such as interactive web mapping and 3D scanning to create an immersive virtual experience. A variety of media will be hosted in this virtual environment such as photos, videos, and audio recordings, collected from each lighthouse in order to best understand these iconic buildings. This research and resultant web tool will empower small coastal communities by providing them new more accessible ways of recording their histories while simultaneously increasing outside intrigue and potentially bolstering their tourism economies and preservation resources.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.171 | 0.069 |
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".