Canadian Coastal Information System (CIS) - Backshore, Beaufort Sea Coast 1984, 1999 and 2000
Bibliographic record
Abstract
Shore-zone classification data for the Canadian Beaufort Sea coast form part of a GIS database, the Coastal Information System (CIS), developed and maintained by the Geological Survey of Canada (GSC-Atlantic), Natural Resources Canada, at the Bedford Institute of Oceanography in Dartmouth, NS. Coastal attributes in the CIS have been interpreted and mapped from low-level, oblique, aerial photography and video imagery with accompanying audio commentary. The CIS is hierarchical in that coastal forms are classified based on four increasingly detailed levels of description. The coastal data consist of line segments with attributes describing the physical form and material at scales ranging from 1:10 000 to 1:250 000. In the CIS, the coast is divided into three shore-parallel zones (backshore, foreshore, nearshore). Each zone is segmented separately based on homogeneous physical characteristics. A glossary has been developed to control the language used to describe each zone. The use of objective physical criteria for coastal classification enhances the utility of the CIS for a wide variety of applications. The backshore is defined as the landward zone lying between higher high-water at large (spring) tides and the upper limit of shore-zone processes; it is acted upon by slope-failure processes, waves, wind, rainfall, or ice and covered by seawater only during severe storms with exceptionally high water levels. It is typically horizontal or rising landward and may be divided from the foreshore by the crest of the most seaward berm. The landward backslope of a spit or barrier beach is also considered to be part of the backshore unless the bay or lagoon shore of the barrier is mapped separately. Typical mapped backshore forms include cliffs, slopes, dunes, washover channels and fans, and wetlands, as well as artificial forms such as docks, ramps, seawalls, revetments, or other shore protection forms. Materials include combinations of ice, organics, mud, sand, gravel and boulders, rock, or artificial materials such as concrete, wood, or steel. Combinations of zonal form and material types can be queried to build more sophisticated classifications based on multiple criteria or empirical indices. For more information please refer to the datafile entitled: CCIN12119_20150218_Backshore_Extended_Metadata_FGDC.xml
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.029 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".