AEON VEMCO Fish Tag Receiver Raw Data
Bibliographic record
Abstract
The AEON bottom landers are equipped with Vemco Fish Finders, which record the tag number of any tagged fish that passes through its vicinity. These are the raw data files. To see more about the fish tags captured by the sensor see: https://members.oceantrack.org/ search on institution is UNH.This collection represents the data collected from the Acoustic and Environmental Observation Network (AEON) in the U.S. Northwest Atlantic, developed and deployed 2021. The AEON network of five observation nodes complements existing oceanographic monitoring infrastructure in the Gulf of Maine. The observatory network provides simultaneous, long-term monitoring of soundscapes and multiple acoustically-relevant parameters such as marine mammal behavior and prey concentration at key locations where changes in the Labrador and Gulf Stream currents are projected to affect the Gulf of Maine.The lead P.I. for this project is Dr. Jennifer Miksis-Olds, University of New Hampshire (UNH). Dr. Miksis-Olds leads a collaborative research team consisting of individuals from UNH, JASCO Applied Sciences, Stony Brook University, and other collaborators.Please cite these data as Jennifer Miksis-Olds, AEON PI, University of New Hampshire with funding from U.S. Department of Defense, Office of Naval Research Awards N00014-20-1-2312, N00014-23-12767, N00014-24-12740.Raw data [including detailed metadata] are archived at the NOAA National Centers for Environmental Information : Active Acoustic raw data, https://www.ncei.noaa.gov/maps/water-column-sonar/ Passive Acoustic raw data, https://www.ncei.noaa.gov/maps/passive_acoustic_data/ The AEON network in the Gulf of Maine totals 5 observation nodes. Datasets are indicated by siteAEON1 NEC, Northeast Channel, CanadaAEON2 ECS, Eastern Coastal Shelf, CanadaAEON3 GEB, Georges Basin, USAEON4 Jordan Basin, USAEON5 Wilkinson Basin, USSampling period is reflected as "Deploy date-Recovery date" (e.g., Jan2022-Feb2023 was deployed January 2022 and recovered February 2023).Dataset manifest:Research Cruise 2: Jasco dataset 1: Feb 2021 thru July 2021 (AEON4 JOB, AEON5 WIB)Research Cruise 3: Jasco dataset 2: July 2021 thru Jan 2022 (AEON3 GEB, AEON4 JOB, AEON5 WIB)Research Cruise 4: Jasco dataset 3: Jan 2022 thru Dec 2022 (AEON1 NEC, AEON2 ECS, AEON3 GEB, AEON4 JOB, AEON5 WIB)Research Cruise 5: Jasco dataset 4: Feb 2023 thru Feb 2024 (AEON1 NEC, AEON2 ECS, AEON3 GEB) and Dec 2022 thru Dec 2023 (AEON4 JOB, AEON5 WIB)Research Cruise 6: Jasco dataset 5: Mar/Apr 2024 thru Apr 2025 (AEON1 NEC, AEON2 ECS, AEON3 GEB, AEON5 WIB)Research Cruise 7 is planned for April 2026Project Website is: https://eos.unh.edu/aeon Contact: "Jennifer Miksis-Olds" See also: https://eos.unh.edu/center-acoustics-research-education Data are supplied as-is and no warranty is made, expressed or implied, regarding these data, nor does the fact of distribution constitute such a warranty.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.089 | 0.068 |
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".