Bibliographic record
Abstract
ABSTRACT The main objective of this paper is to demonstrate how Environmental Sensitivity Index (ESI) maps are made. The efforts have been based on the on-site research and various kinds of lessons from the oil spill from the Russian tanker Nakhodka that happened in January 1997. In September 1999 and March 2000, 168 km of the oil-stranded shoreline was surveyed. This survey was conducted by using the Shoreline Oiling Summary (SOS) Form developed by Environment Canada in 1994. Eighty oiled sites were selected for this survey, and oil residue was found in over 80% of the study sites. It has become clear that the amount of residual oil depends on these four parameters of the survey: (1) types of shoreline sediment, (2) scale of the shoreline, (3) existence of sheltering rocks, and (4) slope of the shoreline. These results almost support the correctness of the ESI guideline developed by the National Oceanic and Atmospheric Administration (NOAA). This guideline, however, is dependent mostly on the calcification of the sediment; for developing ESI maps, parameters 2 and 4 are inevitably ignored. This paper presents one style of an ideal ESI map based on ArcView® 3.2, one of the common GIS platforms.
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 | 0.013 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.024 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".