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Continental Cap-and-Trade: Canada, the United States, and Climate Change Partnership in North America

2010· article· en· W10843675 on OpenAlexaboutno aff
Jessie Childs

Bibliographic record

VenueHouston journal of international law · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
FundersNational Cancer Institute
KeywordsInternational tradeClimate changeEmissions tradingGeneral partnershipPolitical scienceBusinessLaw

Abstract

fetched live from OpenAlex

In this study, we show that administration of low-dose melphalan (l -PAM, l -phenylalanine mustard) to mice bearing a large MOPC-315 plasmacytoma led to a rapid up-regulation of B7-1 (CD80), but not B7-2 (CD86), expression on the surface of MOPC-315 tumor cells. This l -PAM-induced preferential up-regulation of B7-1 surface expression was due, at least in part, to a direct effect of l -PAM on the tumor cells, as in vitro exposure of MOPC-315 tumor cells to l -PAM led to the preferential up-regulation of B7-1 surface expression. Moreover, in vitro exposure of MOPC-315 tumor cells to two other anticancer modalities, gamma-irradiation and mitomycin C, resulted in the preferential up-regulation of B7-1 surface expression. This effect was not restricted to MOPC-315 tumor cells, as preferential up-regulation of B7-1 surface expression was observed also following in vitro exposure of the P815 mastocytoma (that is negative for both B7-1 and B7-2 surface expression) to any of the three anticancer modalities. The up-regulation of B7-1 surface expression following in vitro exposure of tumor cells to l -PAM, gamma-irradiation, or mitomycin C required de novo protein and RNA synthesis, and was associated with the accumulation of mRNA for B7-1 within 4-8 h, indicating that the regulation of B7-1 expression is at the RNA transcriptional level. These results have important implications for an additional immune-potentiating mechanism of these anticancer modalities in clinical setting.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.793
Threshold uncertainty score0.168

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.018
GPT teacher head0.273
Teacher spread0.254 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations1
Published2010
Admission routes1
Has abstractyes

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