GIS-Based （W^＋ - W^- ） Weight of Evidence Model and Its Application to Gold Resources Assessment in Abitibi, Canada
Bibliographic record
Abstract
模型广泛地为印射的矿物质潜力被使用了的证据(WofE ) 的重量。在变换期间一多，进二进制代码的类地图印射很多矿化作用信息人工地被增加或因为在到一个线性特征的累积距离间隔以内的类的归纳基于一个最大的对比，输了，它匹配累积距离间隔。另外，因为一个最大的对比不存在，一些范畴的数据证据不能被这个方法产生。在这篇文章，一种选择(W (+)—W (?)) 基于的 WofE 模型被建议。在这个模型，“(W (+)—W (?)) 比零大“被用作一个标准到重新分类相应的范畴的班进存在或缺席班变换一多，进二进制代码的班地图印射。这个模型能被用于范畴的数据和连续数据。后者能作为范畴的数据被操作。产生二进制地图的 W (+) 和 W (?) 能是 recalculated，和几张二进制地图能在 reclassified 二进制代码证据有条件地独立于对方的条件上综合。这个方法有效地减少人工的数据和名字、范畴的数据罐头被操作。在 Abitibi 区域印射的金潜力的案例研究，安大略，加拿大，金潜力印射由的表演(W (+)—W (?)) 模型显示一个更小的潜在的区域但是更高的后验概率(流行音乐) ，而潜力印射由传统(W (+)—W (?)) 模型展出一个更大的潜在的区域但是更低的流行音乐。给词调音：WofE；形成对照；(W (+)—W (?)) 基于；印射的金潜力
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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 | 0.016 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.022 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".