美國南達科他州和懷俄明州黑丘陵真亞口魚Catostomus catostomus（Catostomidae）隔離族群之初歩評估
Bibliographic record
Abstract
[[abstract]]真亞口魚（Catostomus catostomus）是在北美分佈最廣的亞口魚，地理分佈範圍包括加拿大，阿拉斯加州，五大湖地區，密蘇里河上游的流域，並延伸到西伯利亞東部。在1996年的黑丘陵（南達科他州和懷俄明州）溪流調查，真亞口魚隔離的，潛在受威脅的，族群被指出，並進一步研究被建議。本研究調查了所有現有的文獻以及未出版的的檔案資料來進行黑丘陵的真亞口魚族群之初歩評估。結果表明了：（1）黑丘陵內真亞口魚的間斷記錄很可能反映實際的地理分佈而不是採樣的不足之處，（2）黑丘陵內真亞口魚的地理分佈與平均溪流流量有很強的關聯，（3）黑丘陵的真亞口魚很可能是更新世冰期孑遺族群。更密集黑丘陵的真亞口魚族群監測，研究，以及保育措施被推薦。 The longnose sucker (Catostomus catostomus) is the most widely distributed sucker in North America, ranging throughout Canada, Alaska, the Great Lakes region, the upper Missouri River system, and extending into eastern Siberia. Isolated, potentially threatened, populations of longnose suckers in the Black Hills, South Dakota and Wyoming, were noted during stream surveys in 1996, and further study to assess their status and conservation requirements was recommended. This study surveyed all available literature and unpublished archival records to make a preliminary assessment of the status of longnose suckers in the Black Hills. Results suggest that: (1) disjunct records of longnose suckers in the Black Hills probably reflect actual geographic distribution rather than sampling deficiencies; (2) distribution of longnose suckers within the Black Hills is strongly related to mean annual discharge of streams; (3) longnose suckers in the Black Hills probably represent Pleistocene glacial relict populations. More intensive monitoring, research, and conservation actions for longnose sucker populations in the Black Hills are recommended.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".