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Record W7104057723 · doi:10.25549/chs-c65-385

2 maps of old Spanish and Mexican ranchos in Los Angeles County, 1919 & 1937

2021· dataset· en· W7104057723 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Southern California Digital Library · 2021
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsBayLatin AmericansLeaguePacific oceanScale (ratio)

Abstract

fetched live from OpenAlex

Photograph of a map prepared by the Title Insurance and Trust Company showing the old Spanish and Mexican ranchos of Los Angeles County, 1919. The Sierra Nevada mountains run along the top of the image, and in the bottom left corner is the Pacific Ocean. Between these features is a maze of lines marking the boundaries of the old ranchos. A compass rose is at left, and the map is decorated with drawings of wagons, ships, fish, people, animals, and buildings. A box in the lower right corner provides a scale for the map in English miles, Spanish leagues and Spanish Varas.; The names of the ranchos and other landmarks are as follows, from left to right, top to bottom: Los Alamos of Agua Caliente, La Liebre, Temescal, San Francisco, Simi, Ex-Mission de San Fernando, Tujunga, La Canada, El Camino Real, Las Virgenes, El Escorpion, El Encino, Providencia, San Rafael, San Pascual, Santa Anita, Azusa de Duarte, Azusa Dalton, Addition to San Jose, El Conejo, San Vicente y Santa Monica, Los Felis, San Francisquito, San Jose, Topanga, Malibu, Sequil, Boca de Santa Monica, San Jose de Buenos Aures Rodeo de Las Aguas, La Brea, Pueblo de Los Angeles, San Gabriel Arcangel, Potrero Grande, Potrero de Felipe Lugo, Los Nogales, Rincon de Los Bueyes, Las Cienegas, La Merced, Potrero Chico, La Puente, Bay of Santa Monica, La Ballona, Cienega Paso de la Tijera, San Antonio, Paso de Bartolo, Rincon de La Brea, Sausal Redondo, Tajauta, Santa Gertrudes, La Habra, San Pedro, Los Cerritos, Los Palos Verdes, Bay of San Pedro, Los Alamitos, Isla Santa Catalina.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.027
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.007
GPT teacher head0.169
Teacher spread0.161 · 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
GenreDataset

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
Published2021
Admission routes1
Has abstractyes

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