Bibliographic record
Abstract
This is the OBIS extraction of the Ocean Tracking Network and Mi’kmaq Confederacy of PEI (MCPEI) Striped Bass tagging in PEI, 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=MCPSB). Abstract:Abundance of striped bass (Morone saxatilis) in the SGSL is high compared to recent historical levels (DFO 2020). The goal of this study is to increase our understanding of striped bass movements and overwintering behaviour in PEI estuaries and within the Southern Gulf. Twenty-one Striped bass (>50 cm TL) caught as bycatch in the commercial gaspereau (Alosa pseudohangengus) were implanted with acoustic transmitters in May-June of 2021. All fish were anesthetized prior to surgery and were allowed to fully recovery before being released at the point of capture. Six VR2W acoustic receivers were deployed the length of the Hillsborough river to monitor bass movements within the system. Preliminary results indicated that the majority of tagged fish left the system over the summer months (July-August) but some individuals remained as late as October. Three fish were captured by anglers and tags were recovered. Many fish were detected by receivers in NS and NB over subsequent months indicating movement to known spawning grounds, consistent with known striped bass behaviour.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".