Bibliographic record
Abstract
Since 2014, the Coastal and Ocean Information Network Atlantic (COINAtlantic) in collaboration with the Canadian node of the Ocean Biogeographic Information System (OBIS) and other academic, government and non-governmental organizations in Atlantic Canada have been rescuing species occurrence data in primary and grey literature and processing it to standards for publication through OBIS. The project has been funded in part by the Atlantic Ecosystem Initiative of Environment and Climate Change Canada and Fisheries and Oceans Canada. The project was awarded Honourable Mention in the 2016 International Data Rescue Award in the Geosciences by Elsevier and the Interdisciplinary Earth Data Alliance. COINAtlantic and OBIS share common goals of promoting and facilitating free and open access to data required for coastal and ocean management. The sharing of data and integration of datasets requires adoption of standards and use of common vocabularies. Manuals, guidelines, and cookbooks can facilitate the process. One of the deliverables of the data rescue project was the release of the first version of “Guidelines for marine species occurrence data rescue – The OBIS Canada Cookbook” in April 2017. This document includes ten recipes ranging from initial identification of sources of data to final project wrap up and lessons learned. A second deliverable was the development of a curriculum for training sessions of custodians of marine species occurrence data. Training is required at all levels in our community. Not only should data be accessible for reuse but also training information and lecture material. This course curriculum, based on the OBIS Canada Cookbook, reused some content already on-line and was tested in a workshop at the 2017 conference of the Atlantic Canada Coastal and Estuarine Research Society. (see Fig. 1). Our curriculum, as presently designed, is an intensive single day, hands on course with a focus on graduate students and early career researchers. The course has nine (9) modules which address the following topics: why we share research data including a general description of and the need for data policies and data management plans and data repositories; an introduction to OBIS and the standards used by OBIS; how to map data sets to Darwin Core terms and how to clean and reformat the data; how to standardize species lists; how to georeference observations and use of gazetteers to standardize location place names; and how to compose standardized discovery metadata. The last module is devoted to the processing of participants’ data sets under the guidance of the instructor. Future activities will include promotion of the use of the cookbook and revision of the recipes according to users’ feedback. The curriculum will be tested again with a new set of participants on an opportunistic basis and modified according to participants’ comments. A staged and edited video of the course is under consideration - the objective is to provide on-line training material. These products will augment the growing number of lesson plans and lecture material made accessible by the OBIS/GBIF community. The resources need to be promoted and reuse encouraged.
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 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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads 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".