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Record W1993467103 · doi:10.5194/os-5-661-2009

Optical tools for ocean monitoring and research

2009· article· en· W1993467103 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOcean science · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring and Analysis
Canadian institutionsDalhousie University
Fundersnot available
KeywordsRadianceEnvironmental scienceRemote sensingBiogeochemical cycleComputer scienceDissolved organic carbonProcess engineeringMaterials scienceEnvironmental chemistryChemistryGeology

Abstract

fetched live from OpenAlex

Abstract. Requirements for understanding the relationships between ocean color and suspended and dissolved materials within the water column, and a rapidly emerging photonics and materials technology base for performing optical based analytical techniques have generated a diverse offering of commercial sensors and research prototypes that perform optical measurements in water. Through inversion, these tools are now being used to determine a diverse set of related biogeochemical and physical parameters. Techniques engaged include measurement of the solar radiance distribution, absorption, scattering, stimulated fluorescence, flow cytometry, and various spectroscopy methods. Selective membranes and other techniques for material isolation further enhance specificity, leading to sensors for measurement of dissolved oxygen, methane, carbon dioxide, common nutrients and a variety of other parameters. Scientists are using these measurements to infer information related to an increasing set of parameters and wide range of applications over relevant scales in space and time.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.108
GPT teacher head0.387
Teacher spread0.279 · 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