Development of a Dynamic Biogeographic Information System for the Gulf of Maine
Bibliographic record
Abstract
Central to the development of an inventory of marine life and improved conceptual understanding of the mechanisms that dynamically shape species distribution patterns needed for renewable resource management is the implementation of strategies aimed at enhancing assimilation and access to existing biogeographical information.Using the internet as a medium and EASy (Environmental Analysis System), an advanced oceanographic geographic information system (GIS) tool, the Gulf of Maine Biogeographic Information System (GMBIS) project will provide a framework for the integration, visualization, analysis and dissemination of diverse types of biogeographical and oceanographic information.End-to-end viability of this approach is demonstrated in the context of a pilot application for the Gulf of Maine, a well-studied ecosystem for which extensive scientific data exist but one that historically has been subject to large-scale perturbation due to overharvesting.Databases at the core of the information system will include those of the Bedford Institution of Oceanography and Atlantic Reference Center, which are the product of multidisciplinary research efforts over the last 30 years in the Gulf of Maine.The development of GMBIS may serve not only as a model for the global Ocean Biogeographic Information System, but it may also will provide a powerful tool to meet the new international and Canadian national demands for integrated and ecologically responsible management of marine resources.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".