Bibliographic record
Abstract
Trade data across multiple databases experience unavailability across some countries/forestry products, inconsistency, and unreliability; and these qualities manifest as discrepancies in the data. Literature provides evidence of discrepancies and inconsistencies within international trade statistics, including documented cases in which they are present within agriculture sector trade. While researchers have worked to pinpoint factors to explain discrepancies, studies on the forestry trade databases are not as prevalent. Therefore, more research needs to be conducted to identify discrepancies within forest sector products trade data to understand the nature of discrepancies found between different bilateral trading partners. The goal of this thesis is to identify and analyze discrepancies in forestry trade data found in bilateral trade series sets for the United States and its top trade partners of forestry products. Discrepancies will be identified using simple mathematical formulations and compared across trade flows and forestry products. Then a unique time-series trade data discrepancy analysis approach is conducted to estimate the nature of trade data discrepancies. The thesis aims to provide a framework for proceeding researchers to utilize in order to apply time-series analysis techniques to trade data discrepancies across any product and country. It also aspires to fill in gaps in the literature of trade data discrepancy analyses that examine forestry product trade data. Results indicate that discrepancies are present between the bilateral import and export quantity statistics for industrial roundwood, sawnwood, plywood and wood chips and particles for the trade flows from Canada to the U.S, Brazil to the U.S, China to the U.S, the U.S. to China, the U.S. to Japan, and the U.S. to the U.K. Augmented Dickey Fuller (ADF) and cointegration tests reveal that each unique time series bilateral trade quantity pair exhibits unique data generating processes. Therefore, estimating the discrepant relationship between bilateral import and export quantities of forestry products does not follow one clear cut method; but most discrepancies can be estimated through simple or multiple linear regressions, vector error correction models (VECM), or auto-regressive distributed lag error correction models (ARDL ECM).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".