North Temperate Lakes LTER: Crayfish Abundance 1981 - current
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Crayfish data include crayfish catch in cylindrical minnow traps baited with beef liver and occasional occurrence in other gear used to sample fish. Traps are placed at fyke net locations in nine study lakes (Allequash, Big Muskellunge, Crystal, Sparkling, Trout, Mendota, Monona, Wingra and Fish). Crayfish traps have been eliminated as gear in the Madison area lakes (Mendota, Monona, Wingra, and Fish) after 2003. Individuals are identified to species and counted. In Trout and Sparkling Lake more detailed surveys have been conducted during the summer on an ad hoc basis to track distribution and abundance of the invading species Orconectes rusticus. In Sparkling lake Rusty Crayfish (Orconectes rusticus) was removed from 2001 to 2008. Catherine L Hein, Brian M Roth, Anthony R Ives, and M Jake Vander Zanden. Fish predation and trapping for rusty crayfish (Orconectes rusticus) control: a whole-lake experiment. Canadian Journal of Fisheries and Aquatic Sciences. 63(2): 383-393. https://doi.org/10.1139/f05-229. Additional data sets consist of pre-LTER sets (initiated in late June 1972) gathered by Capelli (Ph.D. dissertation) and Lorman (Ph.D. dissertation). Most of pre-LTER data is detailed distribution in Trout Lake, and community composition in other area lakes. Sampling Frequency: annually Number of sites: 9 Note that 2020 data does not exist due to insufficient sampling.
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.
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) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.132 | 0.025 |
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 it