Reply to the Discussion by H. Jiang and B. Fu of “Prediction of energy absorption capacity of un-cracked and cracked concrete elements through three-point bending tests”<sup>1</sup>Appears in the Canadian Journal of Civil Engineering, <b>39</b>(10): 1161–1162 [doi: 10.1139.l2012-089]
Bibliographic record
Abstract
In the discussion, the discussers are concerned about the applicability of the suggested model in the paper for the cases of torsional loading and reinforced concrete. The authors want to mention that the above paper provides a general methodology to calculate the total energy absorption capacity of concrete elements, which includes typical beams and columns. For concrete elements subjected to torsion, the torsional shear stress equations in Mechanics of Solids or Elasticity textbook could be adopted without much effort, for the cases of pure torsion and combined torsion, axial loading, and bending. Prediction of the energy absorption capacity of reinforced concrete elements could be also conducted with inclusion of the perfect elastic-plastic behavior of steel rebar. All the stress calculations in classical reinforced concrete can be used to sketch the complete load displacement of the element and further predict its energy absorption capacity.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.037 | 0.044 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".