Fish and Fishing in Holocene Cis-Baikal, Siberia: A Review
Bibliographic record
Abstract
Eastern Siberia's Lake Baikal and its tributaries are productive fisheries, and the region's Holocene archaeological sites confirm that this is a long-standing phenomenon. Recent zooarchaeological investigations of sites here allow Holocene fishing practices to be examined in more detail than was previously possible. Along much of the lake's coast, bathymetry is very steep and the water very cold; here fishing appears to have been supplemental to other subsistence practices such as sealing and ungulate hunting. In shallower areas, waters were warmer and supported very productive fisheries for littoral species, perhaps through the use of nets or traps. The region's rivers offered their own resident species but also were used as spawning grounds by some lake fishes. The lake's littoral fisheries, while productive, likely produced fish throughout the year and did not require complex labor organization to be effectively used. Some sections of the region's rivers, particularly those that were spawning grounds for some lake fishes, may have required more complex sociopolitical organization to be exploited efficiently. Such fish runs were short-lived and the best fishing places likely were spatially restricted. This potentially created the need for pools of labor, required organization of harvesting and processing, and generated surpluses that could be stored and manipulated.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".