MétaCan
Menu
Back to cohort
Record W7070162094

Bulk Trades, the

2013· other· en· W7070162094 on OpenAlexaboutno aff

Bibliographic record

VenueThinkTech (Texas Tech University) · 2013
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsParaphernaliaNucleofectionTSG101LimitingWork (physics)Filter (signal processing)
DOInot available

Abstract

fetched live from OpenAlex

In these files, there have been numerous photographs showing ships engaged in the fishing trade,
\nthe salmon trade, and the lumber trade. The last days of commercial sail were filled with three
\nproblems that grew to overwhelming proportions: a labor problem, a paying freight problem
\n(how does the ship pay its way?), and the biggest problem of them all—competition from more
\nefficient systems, whether rail or powered ship. When compound steam engines became the
\nnorm, rather than the exception, commercial sail could no longer compete with known arrival
\nschedules and rapid turn-around time. Instead, sail could compete best with bulk cargoes where
\nfree warehousing (several months in the hold of a sailing ship) was the norm. From the 1880s
\nthrough the 1920s, another persistent challenge was to find a crew with a skill set commensurate
\nwith the rigors of a large sailing vessel. The smart able-bodied sailors opted for the easier life in
\nsteam. While there was no want of capable officers, the lack of competent sailors became
\ncritical well before 1900. In a brief article I read from the San Francisco Call Bulletin around
\nthe turn of the century, there was a brief newsy note about a well-known master of a big 4-
\nmasted bark that was “day sailing” around the Bay trying to get his newly-hired (shanghaied?)
\ncrew into some sort of sail-drill shape before venturing forth on a voyage. While that kind of
\ntraining is laudable, it is also revealing of the lack of sailing fundamentals available wharfside in
\nlatter-day sail.
\nWhat were the cargoes available to sail in its latter days? Photograph 106a, a colored postcard,
\nis illustrative. Front and center is a French-built bark at the coal wharves in San Francisco south
\nof Market Street. Sailing ships routinely carried coal up and down the west coast and across the
\nPacific and it was a paying “retirement” trade for older sailing vessels. The historic clipper
\nDashing Wave was a particularly fetching coal tramp in its latter days and there are several
\nphotographs of this clipper in and around San Francisco hauling coal long after the glory days.
\nIronic that sailing ships carried the fuel that nourished the boilers of steam engines that were
\nputting the windships out of business. However, such was the reality.
\nGrain was another bulk cargo, and odd as it may seem now, California was once a world
\nbreadbasket for grains of several kinds. Wooden ships carried the wheat through the 1870s and
\n1880s back to the east coast. Starting in the 1870s, European (primarily British, but not
\nexclusively so) sailing ships loaded wheat for Europe and by the end of the 1880s dominated the
\ntrade.
\nPhotographs 106b and 106c are of another boom bulk cargo that lasted well into the twentieth
\ncentury and still prevails—lumber. Photograph 106b shows Port Blakely, Washington, awash in
\nlarge latter-day sailing ships loading finished lumber for ports all around the world. The Hall
\nBrothers maintained an important shipyard co-located with Port Blakely. Sailing ships hauled
\nfinished and semi-finished lumber to Hawaii, Australia, and all over the Pacific. The streams of
\nlumber ships after the 1906 earthquake and fire were prodigious. Photograph 106c (taken 26
\nJuly 1910) reveals, even though faded, large sailing ships (and a steamer to the right) loading
\nlumber from the Hastings Mill at Vancouver, British Columbia.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.315
Threshold uncertainty score0.450

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0030.001
Scholarly communication0.0090.004
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.6850.502

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 head0.012
GPT teacher head0.191
Teacher spread0.179 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

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
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueThinkTech (Texas Tech University)French-language works237,207