Interaction term is significant WITHOUT main affects... AND main effects are significant WITHOUT interaction term?

I am trying to determine the effect of a person's weight and the incline that they are running over on their running speed. I'm just using a simple linear model in R, but I get a weird situation where these two main effects (when viewed without an interaction term) are both significant (and interaction isn't), but when I view the interaction term by itself without main effects, then IT becomes significant! How do I choose between these two conflicting models?

Here's the full model, where neither predictor variable appears significant.

Call:
lm(formula = speed ~ actual.weight * incline, data = wow)

Residuals:
Min        1Q    Median        3Q       Max
-0.311468 -0.101650  0.000843  0.092570  0.307654

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)            1.2301738  0.0353404  34.809   <2e-16 ***
actual.weight         -0.0247079  0.0230644  -1.071    0.287
incline               -0.0004380  0.0005993  -0.731    0.467
actual.weight:incline -0.0005566  0.0003970  -1.402    0.164
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1288 on 102 degrees of freedom
Multiple R-squared:  0.1859,    Adjusted R-squared:  0.162
F-statistic: 7.766 on 3 and 102 DF,  p-value: 0.0001011

Since nothing seems to be significant in the full model, I remove the interaction term and see what if things look different:

Call:
lm(formula = speed ~ actual.weight + incline, data = wow)

Residuals:
Min       1Q   Median       3Q      Max
-0.31216 -0.10062  0.00313  0.08915  0.31215

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)    1.2618681  0.0272936  46.233  < 2e-16 ***
actual.weight -0.0496668  0.0147356  -3.371  0.00106 **
incline       -0.0011274  0.0003442  -3.275  0.00144 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1294 on 103 degrees of freedom
Multiple R-squared:  0.1703,    Adjusted R-squared:  0.1541
F-statistic: 10.57 on 2 and 103 DF,  p-value: 6.693e-05

However, I have some reason to believe that there might be a lone interaction term without main effects. I tested this, just to be safe, and there was significance!

Call:
lm(formula = speed ~ actual.weight:incline, data = wow)

Residuals:
Min       1Q   Median       3Q      Max
-0.30143 -0.09795 -0.00455  0.09431  0.31798

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)            1.1981665  0.0159965  74.902  < 2e-16 ***
actual.weight:incline -0.0008925  0.0001889  -4.726 7.22e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1283 on 104 degrees of freedom
Multiple R-squared:  0.1768,    Adjusted R-squared:  0.1689
F-statistic: 22.33 on 1 and 104 DF,  p-value: 7.218e-06

These models aren't nested, and I'm really confused how to distinguish between them. How are weight and incline really affecting speed?

Lastly, all models explain a little variance in the response variable: Your $$\text{R}^2$$ ranges from 17% to 19%. That means that even if all presumed effects were significant, they don't have a substantial effect.