UBC Institute of Fisheries Field Record B.C. 57-102L
Bibliographic record
Abstract
[header:] The University of Michigan, Ann Arbor, Michigan U.S.A. 48109, Museum of Zoology, Division of Fishes; [handwritten note] Don - if Cas is around could you please take care of this? Thanks, Bob; June 26, 1985; [recipient address:] Dr. C. C. Lindsey, Institute of Animal Resource Ecology, University of British Columbia, Vancouver, B.C. V6T 1W5, Canada; Dear Cas: In 1958, we received (Acc. 1958-VII:29) some of the Mexican fishes collected during the 1957 cruise of the "Marijean", reported on by Karl Ricker in 1959. [paragraph break] Two lots of poeciliids were retained, UMMZ 173824-825, from Field No. BC 57-102, Petacalco Bay, just east of the mouth of Rio Balsas. [paragraph break] We have neither a date of this collection nor name(s) of collector(s). Both collections contain newborn young, so date of collection is especially important. Why the information was not requested in 1958 I have no idea. Unfortunately, Karl Ricker's report on these collections gives no dates. [paragraph break] Could you please supply the collection date and collector(2) (= "Marijean" or ?). Thanks very much. [paragraph break] Great news about your award. See you in Stockholm.; Cordially, Robert R. Miller, Curator of Fishes; [footnote:] RRM:cgz, cc: Don McPhail, Accession file
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.792 | 0.730 |
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".