Mackerel tracking in the Northwest Arm
Bibliographic record
Abstract
This is the OBIS extraction of the Ocean Tracking Network and Dalhousie University (DAL) Mackerel tracking in the Northwest Arm, consisting of the release tagging metadata, i.e. the location and date when the tagged animal was released, and summarized detection events of tagged individuals. If readers are interested in the source dataset they may also inquire with the project PIs as listed here or on the OTN web site (https://members.oceantrack.org/project?ccode=V2LNASTI). Abstract:The recent decisions to close Atlantic mackerel commercial and bait fisheries in Canada to promote stock rebuilding have drawn attention to knowledge gaps in the species' biology. Electronic tagging of mackerel can have an important role in documenting stock structure and movements and estimating key demographic parameters to enhance understanding of stock status toward more effective fisheries management. Electronic tagging has been used in the provision of fisheries management advice in many jurisdictions. It could provide similar benefits for mackerel if effective methods for handling and tagging the species can be developed. Mackerel are very sensitive to handling and efforts to track them with electronic tagging have been limited. In this project, we will develop tagging protocols using a field pilot in the Northwest Arm to evaluate survival of mackerel and track behaviour within the V2LNASTI array.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 0.030 |
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".