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Record W7019524281

Greenland key to saving Atlantic salmon, U.S. says

2016· other· en· W7019524281 on OpenAlexaboutno aff

Bibliographic record

VenueInternet Archive (Internet Archive) · 2016
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsWildlifeSound (geography)Fish <Actinopterygii>PopulationSTREAMSOncorhynchus
DOInot available

Abstract

fetched live from OpenAlex

More dams need to be removed in Maine so salmon can spawn, according to wildlife officials. Saving Atlantic salmon will require the cooperation of fishermen in Greenland, according to the U.S. government. Salmon were once found from the Long Island Sound to Canada. The National Oceanic and Atmospheric Administration, or NOAA, says what's left of the wild population in the U.S. now lives in only a handful of rivers and streams in central and eastern Maine. The fish leave Maine for the sea in the spring, and most eventually end up off Greenland - where fishermen catch about 45 tons of salmon a year. A NOAA report says reducing Greenland's salmon fishery is critical to saving the salmon and more dams in Maine also need to be removed so salmon can spawn.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.560
Threshold uncertainty score0.628

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.5600.432

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 head0.013
GPT teacher head0.251
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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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