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Keep the Rubber Side Down!

2015· article· en· W2792881925 on OpenAlexaboutno aff
David B. LaFrance

Bibliographic record

VenueAmerican Water Works Association · 2015
Typearticle
Languageen
FieldNursing
TopicNursing Education, Practice, and Leadership
Canadian institutionsnot available
Fundersnot available
KeywordsRumbleClubSilenceHeading (navigation)ThunderApartmentHistoryBusinessEngineeringOperations managementArtMedicineCivil engineering

Abstract

fetched live from OpenAlex

The silence is suddenly broken and the rumbling grows louder until it reaches a chaotic thunder and then you see it ‐ not one but dozens and dozens of motorcyclists heading your way. For AWWA there is one motorcycle group above all others, and it does not fall neatly into the category of “not having a care in the world.” In fact it is quite the opposite for the hog‐riding water professionals affectionately known as the Water Buffalos. I am talking about the motorcycle club that annually travels from all parts of the United States and Canada to AWWA's Annual Conference and Exposition (ACE). This year, as the Water Buffalos rumble into ACE15 in Anaheim, Calif., they also celebrate a decade of riding as a group. Their annual journey to ACE is appropriately called Ride With Purpose. And unlike some motorcycle clubs, the Buffalos' purpose is a noble one.

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)0.006
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 score0.396
Threshold uncertainty score0.862

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0060.002
Scholarly communication0.0050.006
Open science0.0010.005
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.3960.409

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 head0.020
GPT teacher head0.288
Teacher spread0.268 · 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
Published2015
Admission routes1
Has abstractyes

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