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Record W6942691694 · doi:10.14286/c7zgcr

UNH - Coastal New England MBON - Atlantic cod

2024· dataset· en· W6942691694 on OpenAlexaff

Bibliographic record

VenueOpen MIND · 2024
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsOcean Tracking Network
Fundersnot available
KeywordsNew englandCoastal zoneTracking (education)Extraction (chemistry)

Abstract

fetched live from OpenAlex

This is the OBIS extraction of the Atlantic Cooperative Telemetry and University of New Hampshire (UNH) UNH - Coastal New England MBON - Atlantic cod, 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=ACT.CNEMBON). Abstract:The Coastal New England project integrates powerful technologies (acoustic telemetry, environmental DNA [eDNA], and acoustics) with traditional fisheries sampling to quantify impacts of changes in local and regional water conditions on individuals, populations, and community structure. Research includes study of the impacts of forage species and environmental conditions on Atlantic cod and common terns in both New Hampshire (Isles of Shoals) and southern Maine (Casco Bay) coastal waters. Collectively, these results demonstrate the value in novel technologies in tracking shifts in biodiversity across space and time. This project’s efforts build on the known strengths of each method (eDNA, diet analyses, active acoustics, and passive acoustics) while also exploring their integration and defining scales of appropriate use. Definitions of how each method describes marine biodiversity in both unique but also shared ways are explored.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.273
Threshold uncertainty score0.543

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0280.018

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.

Opus teacher head0.030
GPT teacher head0.267
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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