International Coastal Atlas Network Newsletter, Vol, 1, Nr 1, March 2012.
Bibliographic record
Abstract
Front page and back page: Stretched and rotated image of 2010 Land Cover from NOAA's Land Cover Atlas.It shows in purple the extent of estuarine emergent wetlands behind the barrier islands, Short Beach Island and Jones Beach Island south of Long Island, New York, USA.The data in this tool were derived through NOAA's Coastal Change Analysis Program (C-CAP).C-CAP uses multiple dates of remotely sensed imagery to produce nationally standardized land cover and land change information for the coastal regions of the U.S.These products provide inventories of coastal intertidal areas, wetlands, and adjacent uplands.The goal is to monitor these habitats by updating the land cover maps every five years.CONTENTS Marine Atlas supports Belgium's Marine Spatial Plan 1 SPINCAM Week in Flanders 4 GSDI Association Marine SDI Best Practice Project Update 5 Scotland's National Marine Plan interactive (NMPi) portal continues to grow and evolve 8 ICAN Tech Newsletter Update 9 About the Network 10 COINAtlantic visualizes the OBIS Canada Integrated Publication Tool entries 10 Marine Atlas supports Belgium's Marine Spatial Plan Figure 1: The Belgian marine spatial plan.N is provided in several formats via several interfaces: Ready-to-use maps (e.g.png images),
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.257 | 0.230 |
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".