Gray Whales off Sakhalin Island, Russia: June - September 2001 A Joint U.S. - Russia Scientific Investigation
Bibliographic record
Abstract
Numerous species of marine mammals inhabit the Sea of Okhotsk. Two of the most endangered populations of large whales in the world; the Okhotsk Sea bowhead whale (Balaena mysticetus) and the western North Pacific (Okhotsk-Korean) gray whale are known to occur in this sea (Brownell et al., 1997; Clapham et al., 1999). Concerns regarding the status of these whale populations have been intensified by the onset of offshore oil and gas development programs in Okhotsk waters. Anthropogenic activities related to oil and gas exploration off the northeastern Sakhalin Island shelf include geophysical seismic surveying, drilling and production operations, waterborne discharges of a variety of materials, seafloor dredging, and vessel/aircraft traffic. These activities pose potential threats to the northeastern Sakhalin marine ecosystem and may impact the critically endangered western gray whale population that annually feeds there (Brownell and Yablokov, 2001; Weller et al., 2002a, 2002c). However, properly conducted biological monitoring can provide the requisite information needed to help prevent significant anthropogenic impacts, and in some cases, such as development of Habitat Conservation Plans (U.S. Fish and Wildlife Service, 1998), assist with mitigating unavoidable ecosystem impacts to acceptable levels. Studies in the U.S. and Canadian arctic or near-arctic, on bowhead whales, white whales (Delphinapterus leucas), and gray whales have demonstrated that knowledge of habitat use and behavioral reactions can help to plan industrial activities in a fashion that allows animals and human development to coexist (summaries in Würsig, 1990; Richardson and Würsig, 1995, 1997). Therefore, it has been recommended by the Russian and U.S. governments that biological investigations of potential industry-related ecosystem impacts off the coast of Sakhalin Island and elsewhere in Russia be conducted concurrent to oil and gas development projects (Anonymous, 1997).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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; both teacher heads 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".