ON FISH, WILDLIFE AND OTHER DOl PROGRAMS
Bibliographic record
Abstract
DOl is directly responsible for managing about a quarter of the U.s. land mass, and natural resources in the Outer continental Shelf (OCS). It manages much of the water resources in the Western u.s. It also is responsible for the economic and social well being of American Indians, Alaska Natives and the peoples of the U.S. territories. If climate changes, DOl would be affected in a variety of ways: o o DOl is the primary manager of the nation's natural ecosystems. It also has special responsibilities in preserving native habitats and species, fish and wildlife, biodiversity, and wetlands. Most assessments indicate that the major adverse effect of climate change will be in precisely these areas. However, some studies also indicate that certain species of fish and wildlife could be more abundant in the event of climate change. DOl is the manager of much and it has a special stake Response. strategies could related programs. of the nation's energy resources, in this nation's energy security. involve changing the emphasis on o Because DOl is the largest landowner in the nation, any responsestrategyinvolvingreforestationor afforestationcan only succeed with active DOl participation. o o DOl manages several areas in or near coasts including Parks, historic structures, and wildlife Refuges which could be affected by sea level rise. DOl manages about 80 % of Alaska which will be profoundly affected by climate change. (Climate change is expected to be magnified as one goes northward). * views expressed here are the author's and not necessarily the Department of the Interior's. 273 o
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.364 | 0.124 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".