Management Decisions for Shoreline Protected Areas: A Nova Scotian Case Study. ” The purpose of this project
Bibliographic record
Abstract
LiDAR data have, traditionally, consisted of x, y, and z points used to build high-resolution DEMs. Recently, however, the intensity of the reflected laser pulses has been incorporated into the data provided by LiDAR surveyors. The exploitation of this information for mapping and analysis has received little attention and the research that has been conducted has dealt with landcover classification for inland targets. As part of a broader research project, this study examines the application of LiDAR intensity data and height metrics to classify the nearshore materials at an ocean beach on the Fundy coast of Nova Scotia using the Nova Scotia Department of Natural Resources shoreline classification scheme. Supervised and unsupervised classifications are performed in order to separate a shoreline at Young’s Cove into bedrock, cobble and sand. Additionally, bedrock covered with barnacles and seaweed at the site is included in the classification. Analysis is performed to investigate possible connections between cover-type and orthometric height, proximity to the ocean and geomorphological change. This last analysis is possible because the LiDAR data available consists of a survey from July of 2000 with last returns and a recent survey from April of 2004, which has first and last returns with intensity values on alternating returns.1
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".