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Record W2037961554 · doi:10.1007/s10872-007-0082-x

Evidence for the grazing hypothesis: Grazing reduces phytoplankton responses of the HNLC ecosystem to iron enrichment in the western subarctic pacific (SEEDS II)

2007· article· en· W2037961554 on OpenAlexaff
Atsushi Tsuda, Shigenobu Takeda, Hiroaki Saito, Jun Nishioka, Isao Kudo, Yukihiro Nojiri, Koji Suzuki, Mitsuo Uematsu, Mark L. Wells, Daisuke Tsumune, Takeshi Yoshimura, Tatsuo Aono, Takafumi Aramaki, William P. Cochlan, Maki Hayakawa, Keiri Imai, Tomoshi Isada, Yoko Iwamoto, William K. Johnson, Sohiko Kameyama, Shungo Kato, Hiroshi Kiyosawa, Yoshiko Kondo, Maurice Levasseur, Ryuji J. Machida, Ippei Nagao, Fumiko Nakagawa, Takahiro Nakanishi, Seiji Nakatsuka, Akira Narita, Yoshifumi Noiri, Hajime Obata, Hiroshi Ogawa, Kenji Oguma, Tsuneo Ono, Tomofumi Sakuragi, Motoki Sasakawa, M. Sato, Akifumi Shimamoto, Hyoe Takata, Charles G. Trick, Yutaka Watanabe, C. S. Wong, Naoki Yoshie

Bibliographic record

VenueJournal of Oceanography · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsWestern UniversityUniversité LavalNorth Pacific Marine Science Organization
Fundersnot available
KeywordsPhytoplanktonSubarctic climateBloomCopepodIron fertilizationGrazingDiatomBiologyBiomass (ecology)Chlorophyll aOceanographyZooplanktonBotanyAnimal scienceEcologyNutrientGeology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.047
GPT teacher head0.262
Teacher spread0.216 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations91
Published2007
Admission routes1
Has abstractno

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