Sensitivity Mapping – With Flare! An Internet Approach to Environmental Mapping
Bibliographic record
Abstract
ABSTRACT During emergencies the ready availability of information on the location and vulnerability of resources at risk is crucial to a successful response and in preventing or minimizing further environmental impacts. Environment Canada, Atlantic Region, has developed over a number of years a computer based GIS mapping system for managing and analyzing environmental information. This stand-alone user friendly mapping application has recently moved to the web; allowing broader access by federal, provincial and industry partners in the spill response field. Enhancements have been made that facilitate better coordination and exchange of data among partners. It incorporates a unique shoreline classification system which can be viewed in concert with biological, human use and logistical data. It includes a spill logging function to manage situation reports, maps, resource summaries, photographs and trajectory model outputs. The system allows thematic layers to be displayed on either topographic maps or hydrographic charts and possesses links to other sites that allow real-time display of weather and ocean current data useful in a response. With an open architecture concept the web mapping system is readily modified; partners are able to digitize on-line and to update their own databases shared on the system. Mapped data for the northeastern United States is also included in the package to facilitate joint response to trans-boundary pollution incidents. Although this paper will highlight the unique features of the web mapping application for planning and responding to environmental emergencies, other partners are using the system for conducting environmental assessments, inland management projects, or planning for nuclear emergencies around the globe.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.008 |
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".