Marine Sciences at the University Of Maine, 1960-2015
Bibliographic record
Abstract
The development of marine science research, teaching, and service at the University of Maine formally began in 1965, when Ira Darling and Clare Shane Darling transferred their 127-acre farm and woodlot on the Damariscotta River in South Bristol to the University. Their express purpose was to establish a marine laboratory. The gift fulfilled the decades old desire by University of Maine scientists and administrators to do just that. UMaine quickly began hiring faculty, starting research projects, building structures, developing courses, and creating ties to state and federal agencies. The transition from farm to world-class facility and laboratory was gradual, with periodic uncertainties over funding and direction. But, eventually, UMaine became one of the first twenty institutions in the United States to achieve “Sea Grant” status while University of Maine Marine Sciences overall became one of the University’s most important units, supporting research in the Gulf of Maine and the oceans beyond. Catherine Schmitt is Communications Director at the Maine Sea Grant College Program. She is the author of A Coastal Companion: A Year in the Gulf of Maine from Cape Cod to Canada (2008) and The President’s Salmon: Restoring the King of Fish and its Home Waters (2015). Her forthcoming book project (2016) is titled Historic Acadia National Park. Shelby Hartin is a 2015 graduate of the University of Maine with a bachelor’s degree in English and Journalism. She currently resides in Bangor and is employed by the Bangor Daily News as a customer service representative and features reporter where she writes for the arts and culture, food, and homestead sections of the paper.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.077 | 0.023 |
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".