Bibliographic record
Abstract
DIRECTOR'S FORWARD. ABSTRACT. INTRODUCTION. Definition of "Jellyfish". Problem Statement. Challenges of Studying Jellyfish Populations. Impacts of Jellyfish Blooms. Invasive Species. MATERIALS AND METHODS. Large Marine Ecosystem Approach. 1950 Baseline. The Jellyfish Chronicles. Data Selection. Abundance Trend. Scoring Chronicles. Identifying Invasive Species. Fuzzy Expert System. Uncertainty. RESULTS. Effects of Invasive Species. Effects of Jellyfish Overexploitation. DISCUSSION. Defining an ‘Increase’. Species Invasions. Taxonomic Concerns. LME #1 – East Bering Sea. LME #2 – Gulf of Alaska. LME #3 – California Current. LME #4 – Gulf of California. LME #5 – Gulf of Mexico. LME #6 – Southeast U.S. Continental Shelf. LME #7 – Northeast U.S. Continental Shelf. LME #8 – Scotian Shelf. LME #9 – Newfoundland-Labrador Shelf. LME #10 – Insular Pacific-Hawaiian. LME #11 – Pacific Central-American Coastal. LME #12 – Caribbean Sea. LME #13 – Humboldt Current. LME #14 – Patagonian Shelf. LME #15 – South Brazil Shelf. LME #16 – East Brazil Shelf. LME #18 – West Greenland Shelf. LME #21 – Norwegian Sea. LME #22 – North Sea. LME #23 – Baltic Sea. LME #24 – Celtic-Biscay Shelf. LME #25 – Iberian Coastal. LME #26 – Mediterranean Sea. LME #28 – Guinea Current. LME #29 – Benguela Current. LME #30 – Agulhas Current. LME #31 – Somali Coastal Current. LME #32 – Arabian Sea. LME #34 – Bay of Bengal. LME #35 – Gulf of Thailand. LME #36 – South China Sea. LME #40 – Northeast Australian Shelf. LME #41 – East Central Australian Shelf. LME #42 – Southeast Australian Shelf. LME #47 – East China Sea. LME #48 – Yellow Sea. LME #49 – Kuroshio Current. LME #50 – Sea of Japan. LME #51 – Oyashio Current. LME #52 – Sea of Okhotsk. LME #53 – West Bering Sea. LME #60 – Faroe Plateau. LME #61 – Antarctic. LME #62 – Black Sea. LME #63 – Hudson Bay. CONCLUSIONS. REFERENCES. APPENDICES. Appendix A - Jellyfish Chronicles. Appendix B - Belief Indexes.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.562 | 0.403 |
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; the direct Gemma label and the distilled Codex classifier 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".