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Record W6980485651

Challenges to California???s Ports

2019· other· es· W6980485651 on OpenAlexaboutno aff

Bibliographic record

VenueCSUN ScholarWorks (California State University, Northridge) · 2019
Typeother
Languagees
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacy and Medical Practices
Canadian institutionsnot available
Fundersnot available
KeywordsPort (circuit theory)Panama canalContainer (type theory)Bridge (graph theory)Current (fluid)PanamaLock (firearm)
DOInot available

Abstract

fetched live from OpenAlex

Over past years, container ships have increased the fastest in size more than any other type of ship.Most mega-ships are too big to be handled at current terminals in California.This challenges the efficiency and speed of operation when ships come into port.Consequences: Congestion, delays, and high C02 emission levels. Panama CanalCalifornia is used as a land bridge for ships instead of passing through the Panama Canal because the original locks were too small for current ships.In 2017, new massive locks at the Pacific and Atlantic ocean entrances that will allow advanced ships easier passage.Consequences: Alternate route for ships from East Asia to Europe, diversion of cargo away from California, and lower productivity. Regional PortsShips can choose to avoid the cost of doing business in California.The Port of Lazaro Cardenas & The Port of Manzanillo (Mexico), and Port Prince Rupert (Canada) offer alternate choices that are less costly than ports in California.These ports give an advantage to shippers wanting to reach inland markets or the East Coast of the United States through rail network connections.

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 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 categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient 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 score0.341
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0030.002
Research integrity0.0030.009
Insufficient payload (model declined to judge)0.0190.077

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 head0.086
GPT teacher head0.371
Teacher spread0.285 · 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; both teacher heads 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
Published2019
Admission routes1
Has abstractyes

Explore more

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